Feeding habits affect the energy accumulation capability of pelagic nektons in the high seas of northwest Pacific

Feeding habits affect the energy accumulation capability of pelagic nektons in the high seas of northwest Pacific | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Feeding habits affect the energy accumulation capability of pelagic nektons in the high seas of northwest Pacific Na Zang, Yushuang Luo, Dongming Lin, Bilin Liu, Xinjun Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7723242/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Energy reserves are critical for survival, reproduction, and resilience to environmental variations for marine organisms. However, the energy accumulation capacity in terms of feeding habit impact remains poorly understood, especially for pelagic nektons in the high seas. Here, we measured muscle energy density, stable isotopes and fatty acids for nine pelagic species in the northwestern Pacific Ocean. We quantified isotopic hypervolume (based on stable isotopes, referred to trophic breadth) and nutritional hypervolume (based on fatty acids, referred to dietary richness) following Hutchinson’s n -dimensional hypervolume framework, and examined their relationships with energy density using Bayesian linear hierarchical models. Results revealed taxon-specific variations in energy accumulation capacity among the nektons. A significant positive allometric relationship was observed between energy density and dietary richness, whereas no significant association was detected with trophic breadth. Notably, energy density was not correlated with any individual fatty acid, suggesting that energy accumulation arises from synergistic interactions among diverse dietary components rather than specific prey item. Furthermore, interspecific variation in scaling exponents reflects niche-specific adaptations linked to life-history strategies. Our findings highlight that dietary richness shapes energy accumulation capability, emphasizing the importance of dietary complexity in mediating energy dynamics and species fitness in marine ecosystems. energy accumulation feeding habits dietary richness trophic breadth fatty acids stable isotopes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The energy reserve is fundamental to the basal functions and physiological activities of living organisms, supporting the energetic processes like growth and reproduction (Clarke 2019 ; Pontzer and McGrosky 2022 ). Adequate energy reserve enhances the adaptability and resilience of an organism to respond to the fluctuations of environment (Chuang et al. 2024 ; Liu et al. 2025 ), because counteracting the disruption of homeostasis caused by environmental stressors requires energy to restore physiological stability (Sokolova 2021 ; Sokolova et al. 2012 ). Crucially, the energy reserves and fluxes among species act as critical components linking diverse organisms with environment (Barnes et al. 2018 ) and profoundly influences the structural and functional stability of ecosystems (Eddy et al. 2021; Zhou et al. 2025 ). Optimal foraging theory predict that predators aim to balance expenditures and benefits from feeding, and maximize energy replenishment efficiency to optimize survival and fitness (Pontzer and McGrosky 2022 ; Prokopenko et al. 2023 ). Predators depend on the availability of prey resources to meet nutritional and energy requirements, making adequate food availability a critical source for energy accumulation (Hossie et al. 2021 ). In the wild, the amount of food available to an organism is finite, but can compensate to some extent by variations in adjusting their selection criteria to focus on prey with high quality (Lin et al. 2022 ). For instance, Argentinean shortfin squid ( Illex argentinus ) shifts to feed on higher trophic prey items as energy demand increases after onset of maturation (Lin et al. 2022 ), and the Steller sea lions ( Eumetopias jubatus ) in Alaska increases the lipids reserve by shifting prey items from lower fat fish to higher energy density prey species (Jeanniard du Dot et al. 2008 ; Trites and Donnelly 2003 ). This preference for high-quality preys inevitably entails substantial energy expenditure to sustain predation behavior and metabolic processes associated with feeding (Machovsky-Capuska and Raubenheimer 2020 ; Whitlock et al. 2015 ). Consequently, feeding habits emerges as a core mechanism regulating the dynamics of energy accumulation in the framework of balancing energy intake and expenditure (García et al. 2018 ). In marine ecosystems, species have evolved divergent feeding strategies to utilize food resources, transitioning from specialists to generalists based on the size of the feeding niche (Pompozzi et al. 2019 ). Generally, specialists narrow dietary breadth to efficiently target high-nutrition prey or different body parts of the prey, enabling directional energy acquisition, such as orcas ( Orcinus orcas ) consuming only the liver of sharks (Ford et al. 2011 ), which is the lipid-rich organ in elasmobranchs (Del Raye et al. 2013 ), and Mediterranean bogue ( Boops boops ) selectively feed on the gonads of mauve stinger jellyfish ( Pelagia noctiluca ) as its high lipid concentration (Giacomo et al. 2014 ). Nevertheless, this specialized feeding habits not only entails longer hunting and higher energy expenditures (Michálek et al. 2017 ) but also inevitably encounters risks associated with environmental fluctuations and food resource scarcity (Colles et al. 2009 ). Conversely, generalists balance nutrient intake by broadening dietary spectrum and support sustained energy homeostasis by maintaining an elevated feeding frequency (García et al. 2018 ; Kohl et al. 2015 ), but they have to entails metabolic costs associated with low capture or handling efficiencies and digesting heterogeneous diets (Michálek et al. 2017 ; Terraube et al. 2011 ). However, the research of how feeding habits influence energy accumulation capability is still open, particularly for species inhabiting in high seas area. This unresolved issue represents a significant knowledge gap in marine ecology, impeding our understanding of the energy requirements and physiological adaptations among marine species. The amount of energy that an organism can assimilate is dependent on its ability of energy accumulation (Sokolova 2021 ), which can be measured by the tissue energy density, defining as energy storage per unit mass (Chen et al. 2020 ; Lin et al. 2017 ). Therefore, the energy density can not only be used as an indicator of a consumer's energy accumulation capability, but also as a critical parameter for assessing fish nutritional condition (Lin et al. 2017 ; López-Pérez et al. 2023 ). An organism usually varies the energy accumulation with the physiological requirements (Anthony et al. 2000 ; López-Pérez et al. 2023 ), such as increasing energy reserves during pre-reproduction (English and Bonsall 2019 ) and/or pre-hibernation period (Bulakhova and Shishikina 2022 ). An efficient energy accumulation capability implies that, under comparable foraging conditions, an individual can rapidly build up energy reserves to endure periods of food scarcity, support high-energy-demand activities (e.g., reproduction (Lin et al. 2022 ), migration (DuRant et al. 2007 ; Tablado et al. 2014 )), or bolster resilience to environmental stressors such as climate change (Queiros et al. 2023 ; Sokolova et al. 2012 ). The high seas area of the northwest Pacific Ocean is one of the most important oceanic systems. It is characterized by high productivity and home for many species, due to the nutrient-rich water masses induced by western boundary (Oyashio and Kuroshio) currents, coupled with the Western Subarctic Gyre (Nishioka et al. 2020 ; Taguchi et al. 2007 ; Wang et al. 2021 ). Many species inhabit and/or forage in the similar water layer in the area during spring and summer seasons (Knauber et al. 2023 ; Zhu et al. 2024 ). Herein, we collected specimens of 9 nekton species (see “Material and Methods”) from this area, and used them as a case study to investigate their energy accumulation capability. We measured the bulk carbon and nitrogen stable isotopes and fatty acids for each species, and applied the Hutchinson’s n -dimensional hypervolume concept (Hutchinson 1957 ) to quantify their isotopic hypervolume and nutritional hypervolume. Further, we evaluated the potential relationship between energy accumulation capability and trophic breadth (isotopic hypervolume) or dietary richness (nutritional hypervolume) by using Bayesian linear hierarchical models. In general, the isotopic niche of each species can be measured by the bulk stable isotopes (δ 15 N and δ 13 C), due to the predictable enrichment with trophic position (Layman et al. 2012 ; Post 2002 ). δ 15 N is typically enriched by about 3‰ per trophic level and δ 13 C remains relatively constant (fractionation rate of 1‰-2‰ per trophic level) (Monk et al. 2023 ; Vanderklift and Ponsard 2003 ). Fatty acids are generally released from ingested lipid molecules during digestion, but are generally taken up by consumer’s tissues in their basic form without degradation, and therefore serve as biochemical markers to reflect the nutritional status (Iverson 2009 ) and dietary intakes over a longer period (Galloway et al. 2015 ). Therefore, the isotopic hypervolume based on stable isotopes reflects the range of trophic positions occupied by a species (trophic breadth), while the nutritional hypervolume based on fatty acids captures the diversity of dietary lipid sources, representing dietary richness. We hypothesized that: 1) the energy accumulation capability is determined by their trophic breadth, while 2) the dietary richness optimizes the energy acquisition and expenditure during the procedure of energy accumulation. The findings will put forward our understanding of the physiological and ecological drivers of energy accumulation in marine nektons, and provide valuable insights into their ecological roles in the energy flux within marine ecosystems. Materials and methods 2.1 Sample collection and processing A total of 254 nekton specimens were collected from the research vessel “SONGHANG” in the high seas area of northwest Pacific Ocean (149° 59′ E ~ 164° 11′ E, 34°49′ N ~ 45° 09′ N) during June to July 2022 and 2023 (Table S1 , Fig. 1 ). The nektons included 5 fish species, being Brama japonica , Notoscopelus resplendens , Sardinops melanostictus , Scomber japonicus and Symbolophorus californiensis , and 4 squid species, being Eucleoteuthis luminosa , Gonatopsis borealis , Ommastrephes bartramii and Onychoteuthis compacta . All specimens were frozen immediately onboard, and shipped to Shanghai Ocean University for further analysis in the laboratory within 1.5 months after collection. After defrosting in the laboratory, each specimen was measured the standard length for fish and dorsal mantle length for squid to the nearest 1 mm, and weighed the body weight to the nearest 1 g. An approximately 5 g of muscle tissue of each specimen (dorsal muscle for fish, ventral muscle for squid) was collected, and frozen at -80℃ for 24–36 h before lyophilization to a constant weight, using a freeze-dried chamber (Scientz-10 N lab lyophilizer, Ningbo Scientz Biotechnology Co., LTD.). The dry muscle tissue was ground into fine powder using Scientz-48 grinder (Ningbo Scientz Biotechnology Co., LTD.) after being weighed to the nearest 0.1 mg. The powdered tissues were used for energy density, stable isotope and fatty acid methyl esters analyses (FAME). 2.2 Fatty acid analysis An approximately 500 mg of powdered muscle tissue was used to extract lipids after the protocol of Bligh and Dyer (Bligh and Dyer 1959 ). The extracted lipids were immediately subject to FAME analysis to minimize the likelihood of contamination and oxidation. The lipid-free tissue was recycled and lyophilized to constant weight for stable isotope analysis. A modification of GAQSIQ method (GAQSIQ 2008 ) was used to carry out the FAME analysis. Firstly, the extracted lipids were incubated with 4 mL of 0.5 mol/L KOH–MeOH at 90 ℃ for 10 min, shaking for 5 s every 2 min. Then, 4 mL BF3/MeOH was added and incubated at 90 ℃ for 30 min, followed by the addition of 4 mL n-Hexane for 2 min incubation. Thirdly, 10 mL saturated NaCl was introduced for stratification at room temperature, and then the upper hexane layer containing FAME was transferred for analyses. FAMEs were determined using an Agilent 7890B gas chromatography coupled to a 5977A series mass spectrometer detector (Agilent Technologies, Inc. USA), equipped with a fused silica 60 m × 0.25 nm open tubular column (HB-88: 0.20 µm, Agilent Technologies, Inc. USA). Individual FAME was identified by retention times and mass spectra with a known concentration internal standard 19:0 (GLC 37, Nu-Chek Prep, Inc.), using helium as the carrier gas, and a thermal gradient programmed from 125 to 250 ℃, with the auxiliary heater at 280 ℃. The amounts of individual fatty acids were expressed as percentages of the total FAs (%). A total of 37 fatty acids were detected, however, only 18 FAs (14:0, 15:0, 16:0, 17:0, 18:0, 16:1n7, 18:1n9c, 18:2n6, 18:3n3, 20:1, 20:2, 20:3n3, 20:3n6, 20:4n6, 20:5n3, 22:1n9, 22:2n6 and 22:6n3) constituted > 0.2% of the total fatty acids, and were selected for statistical analyses following (Table S2). 2.3 Energy density analysis Energy density (ED) of muscle tissue was measured before extract lipids. An approximately 300 mg of powdered muscle tissue were used to determine the energy density (KJ/g), using an automatic isoperibol calorimeter (Model 6400, Parr Instrument Company, Moline, IL, USA). The calorimeter system was calibrated with the combustion of benzoic acid as the standard. Briefly, the powdered tissue was gently added to a capsule and then placed into the capsule holder of the calorimeter, which allows for automatically determining the energy density within 15 minutes. 2.4 Stable isotope analysis An approximately 0.3 mg lipid-free tissues were used to determine carbon and nitrogen stable isotopes. The powdered and lipid-free tissue was placed in a tin capsule and analyzed using a SerCon Integra 2 integrated elemental analyzer and an isotope ratio mass spectrometer (EA-IRMS) at the Stable Isotope Core Laboratory in Third Institute of Oceanography (Ministry of Natural Resources, China). The standard substance used for determining δ 13 C was Vienna Pee Dee Belemnite (V-PDB), and the standard substance used for determining δ 15 N was atmospheric nitrogen (N 2 ). The values of δ 13 C and δ 15 N were determined using the following equation: $$\:{{\delta\:}}^{13}\text{C}/{{\delta\:}}^{15}\text{N}=\left(\frac{{\text{R}}_{\text{s}\text{a}\text{m}\text{p}\text{l}\text{e}}}{{\text{R}}_{\text{s}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}}}-1\right)\times\:1000$$ where R sample and R standard represent the ratios of 13 C/ 12 C and 15 N/ 14 N of the sample and the standard reference material, respectively. Triplicate measurements indicated that the measurement errors were approximately 0.2‰ for δ 13 C and δ 15 N. 2.5 Statistical analyses Significant differences were tested for the values of energy density, δ 15 N, δ 13 C and fatty acids between species. All data were checked for normality using a one-sample Kolmogorov-Smirnov test and for homogeneity of the variances using the Levene’s test. When the normality was satisfied, one-way analysis of variance (ANOVA) was applied to determine the significant difference between species, followed by Tukey’s honestly significant post-hoc test. When normal distribution and/or homoscedasticity were not achieved, data were subjected to a Kruskall-Wallis nonparametric test and a Games-Howell post hoc test was performed. A permutational ANOVA (PERMANOVA) with 10000 permutations was used to access the dissimilarity of the fatty acids composition among species. All analyses were performed using the base and vegan package (v. 2.6.8) in R (R Core Team 2023 ). The significant difference was considered when the p value was < 0.05 for the statistical analyses. Niche metric estimation . The trophic niches for each species were estimated using the Stable Isotope Bayesian ellipses algorithm implemented in R (SIBER (v. 2.1.9), (Jackson et al. 2011 )). The Bayesian approximation of the standard ellipse area (SEA b ) and the standard ellipse area corrected by sample size (SEA c , an ellipse containing 40% of the data) was estimated based on 1,000 replications (Jackson et al. 2011 ). SIBER was also applied to estimate the nutritional niches (SEA b and SEA c ) from the first two dimensions of a classical multidimensional scaling (MDS) applied to the 18 fatty acids. MDS is an algorithm suitable for pattern recognition (Delicado and Pachón-García 2024 ). The SEA b were analyzed to test for the significant differences of niche space between species using ANOVA when the data satisfied the assumption of normality, and the Kruskall-Wallis nonparametric test when the normality assumption was rejected. Trophic breadth and dietary richness . The trophic breadth and dietary richness of each species was represented separately by the size of isotopic hypervolume and nutritional hypervolume, computed using Hutchinson’s n -dimensional hypervolume (Blonder 2018 ; Hutchinson 1957 ). The n -dimensional hypervolume highlights the importance of a multidimensional approach when studying ecosystem stability (Blonder et al. 2018 ; Stowasser et al. 2006 ), and have been applied to uncover the individual environmental niches of white storks ( Ciconia Ciconia ) (Carlson et al. 2021 ) and individual dietary specialization of squid(Lin et al. 2025 ). The isotopic hypervolume and nutritional hypervolume for each species were estimated separately for stable isotopes and essential fatty acids using a Gaussian kernel density approach (Blonder 2018 ). The carbon and nitrogen stable isotopes were standardized using z-scores when estimating the isotopic hypervolumes; while the essential fatty acids were scaled using an MDS, with the first two dimensions of the MDS used to estimate the nutritional hypervolumes. Bayesian linear hierarchical models. We employed Bayesian linear hierarchical models to investigate the relationship between energy density and trophic breadth or dietary richness. The model structure was specified as: ln (ED) = ( β₀ + γ₀ ) + ( β₁ + γ₁ ) X + ε where ln (ED) is the natural log-transformed energy density, X is the z-score standardized trophic breadth or dietary richness, β₀ is the fixed-effect intercept, γ₀ represents vectors of random-effect coefficients that account for residual intercept deviations attributable to species uniqueness, β₁ is the fixed-effect slope for the standardized hypervolume (either trophic breadth or dietary richness), γ₁ denotes vector of random-effect coefficients that account for residual slope deviations attributable to species uniqueness, and ε is the model unexplained residual variation. This formulation allows for variation in both intercepts and slopes across species, accommodating heterogeneity in physiological or ecological constraints among taxa. Further, stable isotopes and fatty acids were used as predictors in Bayesian linear hierarchical models to examine the impact of feeding selection on energy density. To identify representative fatty acid predictors, a non-metric multidimensional scaling (NMDS) analysis was conducted on the standardized fatty acids variables using Bray-Curtis dissimilarity and vector fitting was applied to assess the relationship between each variable and the NMDS configuration (999 permutations, Spearman rank correlation). Only variables that exhibited both a statistically significant association ( p 0.7) were retained. The posterior distributions of model parameters were estimated using Markov chain Monte Carlo (MCMC) methods using the “brms” R package version 2.22.0. Models were fitted using four MCMC chains of 15000 steps, including 7500-step warm-up periods, so a total of 30,000 steps were retained to estimate posterior distributions. Convergence was assessed using the Gelman-Rubin statistic (Rhat < 1.01). This modeling approach accounts for non-independence among observations from the same species while estimating both average effects and interspecific variation in response. Results 3.1 Stable isotopes and isotopic niche The 9 species exhibited δ 15 N values from 4.04‰ to 13.92‰ (mean ± sd, 9.93 ± 2.27‰), and δ 13 C values from − 21.74‰ to -18.45‰ (mean ± sd, -19.97 ± 0.59‰; Table S2). Both δ 15 N ( χ 2 = 365.67, p < 0.05) and δ 13 C ( χ 2 = 203.59, p < 0.05) varied significantly among the 9 specie (Fig. 2 a). B. japonica and G. borealis were determined the greatest value of δ 15 N values, whereas S. melanostictus was determined the lowest δ 15 N values (Table S2). E. luminosa and O. bartramii were determined the greatest value of δ 13 C values, whereas S. melanostictus was determined the lowest δ 13 C values (Table S2). The isotopic niche analysis showed that some pairwise overlap was found for all species in the high seas area of northwest Pacific Ocean (Fig. 2 a). The Bayesian approximations of the standard ellipse area (SEA b ) for the isotopic niches differed significantly between species ( χ 2 = 317.67, p < 0.05; Fig. 2 b). The S. japonicus showed the largest SEA b (2.14 ± 0.27), followed by S. melanostictus (2.10 ± 0.26), N. resplendens (1.18 ± 0.26) and S. californiensis (1.17 ± 0.32), while O. bartramii showed the smallest SEA b (0.49 ± 0.06) (Fig. 2 b). 3.2 Fatty acids and nutritional niche There were significant differences in each individual fatty acid among the 9 species (Table S3). The overall FAs also differed significantly among species (PERMANOVA, F = 64.09, p < 0.05). The first two MDS axes for the FAs data explained 68.58% and 13.14% of the overall variation for a total of 81.72% (Fig. 3 a). The nutritional niche analysis showed that each squid species occupies a distinct and narrow nutritional niche, while some of the fish species overlap in nutritional niche (Fig. 3 a). The estimated SEA b values varied significantly among species ( χ 2 = 315.76, p < 0.05; Fig. 3 b). The S. melanostictus showed the largest SEA b , followed by N. resplendens and S. californiensis , while G. borealis showed the smallest SEA b (0.0002 ± 0.00) (Fig. 3 b, Table S4). 3.3 Energy density The energy density values of the 9 species ranged from 17.80 kJ/g to 30.59 kJ/g (mean ± sd, 21.40 ± 2.46 kJ/kg; Fig. 4 , Table S2). There were significant differences in the energy density among species ( χ 2 = 324.73, p < 0.05; Fig. 4 ). N. resplendens and S. californiensis were determined the greatest value of energy density, whereas E. luminosa , G. borealis , O. bartramii and O. compacta showed the lowest energy density value (Fig. 4 ). 3.4 Energy density relation to trophic breadth and dietary richness The trophic breadth, estimated using stable isotopes, ranged from 2.58 to 10.36 across the nine species (Table S5) and exhibited unsignificant positive allometric relationship with muscle energy density (average scaling exponent = 0.09; 95% CIs − 0.10 to 0.29, Fig. 5 a, Fig. 5 b). In contrast, dietary richness estimated by FAs varied from 0.001 to 0.10 across the 9 species (Table S5) and showed a significant positive allometric increase with muscle energy density (average scaling exponent = 0.14; 95% CIs 0.02 to 0.25, Fig. 5 c). There was a variation in the species-specific exponents derived from the model for nutritional hypervolumes, indicating that the relationship between nutritional hypervolume and energy density differs among species (Fig. 5 d, Table S6). S. californiensis (0.21, 95% CIs 0.03 to 0.41) and N. resplendens (0.21, 95% CIs 0.04 to 0.39) exhibited the highest exponent efficiency, while E. luminosa (0.12, 95% Cis − 0.04 to 0.27) had the lowest value (Fig. 5 d, Table S6). Discussion In the wild, energy reserve is essential for survival of organisms throughout their life such as reproduction, migration and overwintering (Clarke 2019 ; Pontzer and McGrosky 2022 ). Empirical evidence has showed that the energy accumulation of an organism is often constrained by both quantity and quality of food resources (Guo et al. 2018 ; Jeanniard du Dot et al. 2008 ), with feeding habits playing a key role in mediating the transfer of external energy into internal allocation (Pontzer and McGrosky 2022 ; Prokopenko et al. 2023 ). Our results reveal significant interspecific variation in muscle energy density among pelagic nektons in the northwestern Pacific Ocean, with a positive allometric relationship between energy density and dietary richness. In contrast, no significant relationship was detected between energy density and trophic breadth. These findings support our second hypothesis that energy accumulation capacity is primarily regulated by the dietary richness, rather than by trophic breadth, providing robust empirical support for the pivotal role of feeding habits in shaping energy dynamics in marine predators. The framework of energy trade-offs under an economic perspective suggests that the energy flow through an organism is not constant, but is affected by the food availability (foraging efficiency), the capacity of energy absorption, and constraints on energy expenditure (Pontzer and McGrosky 2022 ). We observed a significant positive allometric relationship between muscle energy density and dietary richness among the nine species in the high seas area of the northwest Pacific. This pattern indicated that generalist species enhance energetic stability by broadening their dietary spectrum, thereby buffering against resource fluctuations and efficiently accumulating energy reserve (García et al. 2018 ; Kohl et al. 2015 ). For instance, S. japonicus , S. melanostictus , N. resplendens and S. californiensis , exploit a wide range of prey including plankton, small crustaceans and fish larvae (Hirai et al. 2017 ; Watanabe et al. 2004 ), remains compensating for limited predatory capabilities through dietary flexibility. This strategy enables optimize nutrient acquisition and sustained energy reserve by consuming diverse prey assemblages (Pontzer and McGrosky 2022 ). Such an approach is particularly advantageous under unstable food conditions, as energy reserves improves the ability to response intense predation pressure and fluctuations in food availability (Koemel et al. 2019 ). The sufficient energy reserves also mitigate risks associated with environmental variability and support individual physiological condition (Peck et al. 2016 ), ultimately enhancing survival probability (Houston et al. 2007 ). Thus, it would be not unexpected that generalist species have the large energy density (Fig. 5 ), and such pattern may be an adaptive trait for those species, consistent with the survival priority principle in ecological energetics (Sokolova et al. 2012 ; Tomlinson et al. 2014 ). In contrast, feeding specialists such as O. bartramii , G. borealis , O. compacta , E. luminosa and B. japonica , occupying narrow feeding niche and exhibiting relatively low energy densities (Fig. 3 , Fig. 4 ), which may have evolved highly specialized foraging syndromes to maximize net energy gain per unit time. The apex species often rely on specific lipid-rich prey preys, such as orcas ( Orcinus orcas ) consuming only the lipid-rich livers of sharks (Ford et al. 2011 ) and Mediterranean bogue ( Boops boops ) target the lipid-rich gonads of mauve stinger jellyfish ( Pelagia noctiluca ) (Giacomo et al. 2014 ). While such diets yield high per-meal energy returns, they entail elevated hunting costs and metabolic rates (Prokopenko et al. 2023 ). To offset energetic expenditures, these predators may increase feeding frequency, which leads to a high standard metabolic rate in active and relatively short fasting periods, resulting in tissue activity and physiological readiness (Bury 2021 ). Consequently, energy may be preferentially allocated to muscle development and locomotor performance to enhance hunting efficiency, rather than accumulate in muscle tissue. Furthermore, due to the extrinsic (resource availability) and intrinsic (physiological limits) constraints on energy flux, the amount of energy available for an organism at a given time is limited, and the allocation of energy must be directed to optimize its survival and offspring production (Sokolova 2021 ). In general, the basal maintenance is the high priority in the energy allocation to support the cellular and organismal homeostasis and carry out other fitness-related functions (Sokolova et al. 2012 ). Although the diet is rich in energetic contents, a significant portion of the energy intake would be preferentially allocate a substantial proportion of assimilated energy towards metabolic maintenance and physiological processes rather than energy reserve (Machovsky-Capuska and Raubenheimer 2020 ; Whitlock et al. 2015 ). This characteristic thus may explain the energy accumulation capacity decreased with decreasing dietary richness for the nine pelagic species in the high seas area of northwest Pacific (Fig. 5 ). Energy accumulation appears to emerge from synergistic interactions among diverse dietary components, rather than being driven primarily by the enrichment of specific prey types. Such phenomenon is supported by the results that no significant relationships were detected between individual fatty acid concentrations and energy density (Figure S3). This phenomenon likely stems from the functional differentiation of fatty acids within organisms (Tocher 2003 ). For example, saturated fatty acids are primarily incorporated into triacylglycerols for energy storage(Parrish 2013 ), long-chain fatty acids play regulatory roles in lipid metabolism (Nakamura et al. 2014 ), while essential fatty acid such as EPA and DHA are preferentially allocated to membrane fluidity, neural function and anti-inflammatory processes (Fuiman and Ojanguren 2011 ; Parrish 2013 ). Given that the metabolic allocation of these fatty acids may prioritize physiological maintenance over energy storage, elevated fatty acid content does not inevitably lead to increased energy density. Therefore, the observed positive relationship between dietary richness and energy density likely reflects the comprehensive metabolic benefits of diversified nutrient intake, as a broader diet provides a more complete suite of metabolic precursors and co-substrates that enhance the efficiency of lipid biosynthesis pathways. This aligns with the geometric framework for nutrition, which posits that organisms optimize fitness not by maximizing single nutrients, but by achieving a balanced intake of multiple dietary components (Simpson and Raubenheimer 2012 ). This explanation of nutritionally synergistic interactions also provides a plausible explanation for the absence of significant correlations between stable isotope ratios (δ¹⁵N and δ¹³C) or isotopic niche metrics and energy density (Fig. 5 a, Figure S2). Stable isotopes reflect long-term averages of basal carbon sources and trophic position (Layman et al. 2012 ; Post 2002 ), which poorly capture short-term variations in diet quality or the intake of specific nutritional components. In contrast, fatty acids serve as direct biochemical substrates for energy metabolism, structural biosynthesis, and signaling, offering a more sensitive and functionally informative proxy of recent diet quality (Iverson 2009 ; Parrish 2013 ). Thus, it is dietary richness of dietary components rather than trophic breadth that more effectively determines energy accumulation capacity. Furthermore, we observed interspecific variation in the strength of relationship between dietary richness and energy density, reflected in species-specific allometric scaling exponents (Fig. 5 d). For example, S. californiensis and N. resplendens exhibited the steepest slopes, indicating high sensitivity of energy accumulation to dietary richness. In contrast, E. luminosa showed the weakest relationship, suggesting greater reliance on specific lipid-rich prey or internal physiological regulation for energy reserve. This divergence likely reflects evolutionary adaptations to distinct ecological niches, such as migratory or seasonally breeding species ( I. argentinus ) must rapidly accumulate energy to meet periodic demands during reproduction or migration (Lin et al. 2022 ). Thus, the observed interspecific/inter-quality groups divergence in energy reserve allocation likely stems from niche specific foraging optimization driven by multidimensional variability in basal resource quality, given substantial differences in digestibility, and biochemical stoichiometry (Galloway et al. 2015 ). Under evolutionary selection pressures, furthermore, consumers develop taxon-specific foraging syndromes to resolve the metabolic trade-off between energy efficiency and nutritional balancing. This trophodynamic optimization process directly modulates life-history trait expression through biochemical pathways linking nutrient intake (Potter et al. 2018 ). Conclusion Our results reveal a dynamic interplay between trophic breadth, dietary richness and energy accumulation capability for the nine pelagic nekton species in the high seas area of northwest Pacific. Consistent with our initial hypotheses that dietary richness is a key driver of energy accumulation in pelagic marine predators. The interspecific divergence in energy accumulation strategies may arise from adaptive feeding optimization. Generalist species achieve higher muscle energy density through a nutritionally diverse diet, enhancing energetic stability and resilience to environmental fluctuations. In contrast, despite often consuming energy-dense prey, specialists exhibit lower energy reserves, likely due to elevated metabolic demands and preferential allocation of energy toward maintenance and locomotor performance rather than energy reserve. Moreover, the absence of significant relationships between individual fatty acids and energy density implicates that energy accumulation influenced by the synergistic interactions among diverse dietary components. Interspecific variation in the scaling relationship further reflects evolutionary adaptations to distinct ecological strategies. Cumulatively, energy accumulation capability represents adaptive outcomes of feeding strategies, where biochemical and physiological optimizations mediate trade-offs in energy allocation, shaping both individual fitness and ecosystem-level energy transfer. Since ongoing change of climate has significantly influenced physiological and ecological aspects of marine organisms, future research should integrate long-term dietary monitoring with physiological assays to environmental drivers of energy allocation strategies. This study advances our understanding of trophodynamic regulation in marine ecosystems and emphasizes the importance of considering nutritional diversity in predicting organismal performance under changing ocean conditions. Declarations Acknowledgements This work is a contribution of the Project on the Survey and Monitor-Evaluation of Global Fishery Resources (Comprehensive Scientific Survey of Fishery Resources at the High Seas), SHOU. We thank the staff members of the Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University for providing assistance at the laboratory. We also thank the undergraduates and crew onboard “SONGHANG” for assisting the samples collection. Author Contributions Na Zang and Dongming Lin conceived the ideas and designed methodology; Na Zang and Yushuang Luo conducted the laboratory experiment; Na Zang and Dongming Lin analyzed the data; Na Zang wrote the manuscript; Dongming Lin, Bilin Liu and Xinjun Chen improved the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Funding information This work was supported by National Natural Science Foundation of China (41876144), and Shanghai Talent Development Funding (2020107) to D.L., and National Natural Science Foundation of China (41876141) and Shanghai Science and Technology Innovation Program (19DZ1207502) to X.C. Ethical approval All specimens were analyzed in the laboratory using methods that are in line with current Chinese national standards, namely Laboratory Animals—General Requirements for Animal Experiment (GB/T 35823-2018). As all material sampled in this work was obtained from research vessel “SONGHANG” and the sample size of nekton collected for research purposes was kept to a minimum. Competing interests The authors confirm there are no competing interests or conflicts of interest with this manuscript. Data availability statement Data supporting the findings of this study are available upon request. Requests for access the data should be directed to the corresponding author Dongming Lin at [email protected] . References Anthony, J. A., Roby, D. D. & Turco, K. R., 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. https://doi.org/10.1016/S0022-0981(00)00159-3. Barnes, A. 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08:29:10","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170413,"visible":true,"origin":"","legend":"","description":"","filename":"MABID25005190structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/a82f3f247817a9a1f6fd31a5.xml"},{"id":94823300,"identity":"37c6ce90-61af-499a-8684-1644a7706ccd","added_by":"auto","created_at":"2025-10-31 06:47:01","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182130,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/f21049f2dae24f8006542af6.html"},{"id":94739727,"identity":"6d9efc9d-81bc-4b50-a4df-43351a535f8a","added_by":"auto","created_at":"2025-10-30 08:29:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe surveyed area and sampling stations for the nekton in the high seas area of northwest Pacific Ocean\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/486b699669cfe6b56dd61842.png"},{"id":94739729,"identity":"8c91a37d-fa59-4e3a-8b9f-77d6bdf02584","added_by":"auto","created_at":"2025-10-30 08:29:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of isotopic niche metrics for nine species in the high seas area of the northwest Pacific Ocean.\u003c/strong\u003e (a) isotopic niche spaces based on nitrogen and carbon stable isotope and (b) Bayesian approximation of the standard ellipse area (SEA\u003csub\u003eb\u003c/sub\u003e) distribution based on nitrogen and carbon stable isotope; The horizontal lines and grey solid points respectively denote the medians and means, while the upper and lower hinges respectively represent the 25th and 75th percentiles. See Table S1 for species abbreviations\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/a91635d5d7f58dbad039bc06.png"},{"id":94823766,"identity":"5d260060-8dd3-4ccb-8a28-d32711cf1a17","added_by":"auto","created_at":"2025-10-31 06:47:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of nutritional niche metrics for nine species in the high seas area of the northwest Pacific Ocean.\u003c/strong\u003e (a) nutritional niche spaces based on fatty acids and (b) Bayesian approximation of the standard ellipse area (SEA\u003csub\u003eb\u003c/sub\u003e) distribution based on fatty acids. The horizontal lines and grey solid points respectively denote the medians and means, while the upper and lower hinges respectively represent the 25th and 75th percentiles. See Table S1 for species abbreviations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/735f63f725abf145319d2903.png"},{"id":94739730,"identity":"22345d8c-c5f6-4e31-af95-ac4cab4ff493","added_by":"auto","created_at":"2025-10-30 08:29:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of energy density for nine species in the high seas area of the northwest Pacific Ocean.\u003c/strong\u003e The horizontal lines and grey solid points respectively denote the medians and means, while the upper and lower hinges respectively represent the 25th and 75th percentiles. See Table S1 for species abbreviations\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/0d7268431f6482e3f08823b7.png"},{"id":94739732,"identity":"807f479e-b6e0-43c9-a0c5-062bb2156c2d","added_by":"auto","created_at":"2025-10-30 08:29:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":88962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe scaling relationships between energy density and trophic breadth or dietary richness for the 9 species sampled in the high seas area of northwest Pacific Ocean.\u003c/strong\u003e (a) energy density relation to trophic breadth; (b) average exponents and 95% CIs (shown by horizontal bars) for 9 species were obtained by combining species-specific posterior estimates from model in (a); (c) energy density relation to dietary richness and (d) average exponents and 95% CIs (shown by horizontal bars) for 9 species were obtained by combining species-specific posterior estimates from model in (c). Equations in the top-left corners depict average fixed effects; 95% CI is Bayesian credible interval for the scaling exponent; Dashed black lines depict average model fits. See Table S1 for species abbreviations\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/2abfd403aa18a791bae119bb.png"},{"id":94827170,"identity":"bc33d594-61dd-41fd-87de-7dce8ac2b001","added_by":"auto","created_at":"2025-10-31 06:55:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1264230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/5276480d-6f97-452f-9b35-cb64cdd94b12.pdf"},{"id":94822806,"identity":"46114f46-2051-4d61-b294-d21a44a0f5d7","added_by":"auto","created_at":"2025-10-31 06:44:17","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":951843,"visible":true,"origin":"","legend":"","description":"","filename":"Electronicsupplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7723242/v1/1132831f964b07b99fcaf1d3.docx"}],"financialInterests":"","formattedTitle":"Feeding habits affect the energy accumulation capability of pelagic nektons in the high seas of northwest Pacific","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe energy reserve is fundamental to the basal functions and physiological activities of living organisms, supporting the energetic processes like growth and reproduction (Clarke \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pontzer and McGrosky \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Adequate energy reserve enhances the adaptability and resilience of an organism to respond to the fluctuations of environment (Chuang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), because counteracting the disruption of homeostasis caused by environmental stressors requires energy to restore physiological stability (Sokolova \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sokolova et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Crucially, the energy reserves and fluxes among species act as critical components linking diverse organisms with environment (Barnes et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and profoundly influences the structural and functional stability of ecosystems (Eddy et al. 2021; Zhou et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOptimal foraging theory predict that predators aim to balance expenditures and benefits from feeding, and maximize energy replenishment efficiency to optimize survival and fitness (Pontzer and McGrosky \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Prokopenko et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Predators depend on the availability of prey resources to meet nutritional and energy requirements, making adequate food availability a critical source for energy accumulation (Hossie et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the wild, the amount of food available to an organism is finite, but can compensate to some extent by variations in adjusting their selection criteria to focus on prey with high quality (Lin et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, Argentinean shortfin squid (\u003cem\u003eIllex argentinus\u003c/em\u003e) shifts to feed on higher trophic prey items as energy demand increases after onset of maturation (Lin et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the Steller sea lions (\u003cem\u003eEumetopias jubatus\u003c/em\u003e) in Alaska increases the lipids reserve by shifting prey items from lower fat fish to higher energy density prey species (Jeanniard du Dot et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Trites and Donnelly \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This preference for high-quality preys inevitably entails substantial energy expenditure to sustain predation behavior and metabolic processes associated with feeding (Machovsky-Capuska and Raubenheimer \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Whitlock et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, feeding habits emerges as a core mechanism regulating the dynamics of energy accumulation in the framework of balancing energy intake and expenditure (Garc\u0026iacute;a et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn marine ecosystems, species have evolved divergent feeding strategies to utilize food resources, transitioning from specialists to generalists based on the size of the feeding niche (Pompozzi et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Generally, specialists narrow dietary breadth to efficiently target high-nutrition prey or different body parts of the prey, enabling directional energy acquisition, such as orcas (\u003cem\u003eOrcinus orcas\u003c/em\u003e) consuming only the liver of sharks (Ford et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which is the lipid-rich organ in elasmobranchs (Del Raye et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Mediterranean bogue (\u003cem\u003eBoops boops\u003c/em\u003e) selectively feed on the gonads of mauve stinger jellyfish (\u003cem\u003ePelagia noctiluca\u003c/em\u003e) as its high lipid concentration (Giacomo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Nevertheless, this specialized feeding habits not only entails longer hunting and higher energy expenditures (Mich\u0026aacute;lek et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) but also inevitably encounters risks associated with environmental fluctuations and food resource scarcity (Colles et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Conversely, generalists balance nutrient intake by broadening dietary spectrum and support sustained energy homeostasis by maintaining an elevated feeding frequency (Garc\u0026iacute;a et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kohl et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), but they have to entails metabolic costs associated with low capture or handling efficiencies and digesting heterogeneous diets (Mich\u0026aacute;lek et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Terraube et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, the research of how feeding habits influence energy accumulation capability is still open, particularly for species inhabiting in high seas area. This unresolved issue represents a significant knowledge gap in marine ecology, impeding our understanding of the energy requirements and physiological adaptations among marine species.\u003c/p\u003e\u003cp\u003eThe amount of energy that an organism can assimilate is dependent on its ability of energy accumulation (Sokolova \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which can be measured by the tissue energy density, defining as energy storage per unit mass (Chen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, the energy density can not only be used as an indicator of a consumer's energy accumulation capability, but also as a critical parameter for assessing fish nutritional condition (Lin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; L\u0026oacute;pez-P\u0026eacute;rez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An organism usually varies the energy accumulation with the physiological requirements (Anthony et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; L\u0026oacute;pez-P\u0026eacute;rez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), such as increasing energy reserves during pre-reproduction (English and Bonsall \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and/or pre-hibernation period (Bulakhova and Shishikina \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). An efficient energy accumulation capability implies that, under comparable foraging conditions, an individual can rapidly build up energy reserves to endure periods of food scarcity, support high-energy-demand activities (e.g., reproduction (Lin et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), migration (DuRant et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tablado et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)), or bolster resilience to environmental stressors such as climate change (Queiros et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sokolova et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe high seas area of the northwest Pacific Ocean is one of the most important oceanic systems. It is characterized by high productivity and home for many species, due to the nutrient-rich water masses induced by western boundary (Oyashio and Kuroshio) currents, coupled with the Western Subarctic Gyre (Nishioka et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Taguchi et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many species inhabit and/or forage in the similar water layer in the area during spring and summer seasons (Knauber et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Herein, we collected specimens of 9 nekton species (see \u0026ldquo;Material and Methods\u0026rdquo;) from this area, and used them as a case study to investigate their energy accumulation capability. We measured the bulk carbon and nitrogen stable isotopes and fatty acids for each species, and applied the Hutchinson\u0026rsquo;s \u003cem\u003en\u003c/em\u003e-dimensional hypervolume concept (Hutchinson \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1957\u003c/span\u003e) to quantify their isotopic hypervolume and nutritional hypervolume. Further, we evaluated the potential relationship between energy accumulation capability and trophic breadth (isotopic hypervolume) or dietary richness (nutritional hypervolume) by using Bayesian linear hierarchical models. In general, the isotopic niche of each species can be measured by the bulk stable isotopes (δ\u003csup\u003e15\u003c/sup\u003eN and δ\u003csup\u003e13\u003c/sup\u003eC), due to the predictable enrichment with trophic position (Layman et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Post \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). δ\u003csup\u003e15\u003c/sup\u003eN is typically enriched by about 3\u0026permil; per trophic level and δ\u003csup\u003e13\u003c/sup\u003eC remains relatively constant (fractionation rate of 1\u0026permil;-2\u0026permil; per trophic level) (Monk et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vanderklift and Ponsard \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Fatty acids are generally released from ingested lipid molecules during digestion, but are generally taken up by consumer\u0026rsquo;s tissues in their basic form without degradation, and therefore serve as biochemical markers to reflect the nutritional status (Iverson \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and dietary intakes over a longer period (Galloway et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, the isotopic hypervolume based on stable isotopes reflects the range of trophic positions occupied by a species (trophic breadth), while the nutritional hypervolume based on fatty acids captures the diversity of dietary lipid sources, representing dietary richness. We hypothesized that: 1) the energy accumulation capability is determined by their trophic breadth, while 2) the dietary richness optimizes the energy acquisition and expenditure during the procedure of energy accumulation. The findings will put forward our understanding of the physiological and ecological drivers of energy accumulation in marine nektons, and provide valuable insights into their ecological roles in the energy flux within marine ecosystems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sample collection and processing\u003c/h2\u003e\u003cp\u003eA total of 254 nekton specimens were collected from the research vessel \u0026ldquo;SONGHANG\u0026rdquo; in the high seas area of northwest Pacific Ocean (149\u0026deg; 59\u0026prime; E\u0026thinsp;~\u0026thinsp;164\u0026deg; 11\u0026prime; E, 34\u0026deg;49\u0026prime; N\u0026thinsp;~\u0026thinsp;45\u0026deg; 09\u0026prime; N) during June to July 2022 and 2023 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The nektons included 5 fish species, being \u003cem\u003eBrama japonica\u003c/em\u003e, \u003cem\u003eNotoscopelus resplendens\u003c/em\u003e, \u003cem\u003eSardinops melanostictus\u003c/em\u003e, \u003cem\u003eScomber japonicus\u003c/em\u003e and \u003cem\u003eSymbolophorus californiensis\u003c/em\u003e, and 4 squid species, being \u003cem\u003eEucleoteuthis luminosa\u003c/em\u003e, \u003cem\u003eGonatopsis borealis\u003c/em\u003e, \u003cem\u003eOmmastrephes bartramii\u003c/em\u003e and \u003cem\u003eOnychoteuthis compacta\u003c/em\u003e. All specimens were frozen immediately onboard, and shipped to Shanghai Ocean University for further analysis in the laboratory within 1.5 months after collection.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter defrosting in the laboratory, each specimen was measured the standard length for fish and dorsal mantle length for squid to the nearest 1 mm, and weighed the body weight to the nearest 1 g. An approximately 5 g of muscle tissue of each specimen (dorsal muscle for fish, ventral muscle for squid) was collected, and frozen at -80℃ for 24\u0026ndash;36 h before lyophilization to a constant weight, using a freeze-dried chamber (Scientz-10 N lab lyophilizer, Ningbo Scientz Biotechnology Co., LTD.). The dry muscle tissue was ground into fine powder using Scientz-48 grinder (Ningbo Scientz Biotechnology Co., LTD.) after being weighed to the nearest 0.1 mg. The powdered tissues were used for energy density, stable isotope and fatty acid methyl esters analyses (FAME).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Fatty acid analysis\u003c/h2\u003e\u003cp\u003eAn approximately 500 mg of powdered muscle tissue was used to extract lipids after the protocol of Bligh and Dyer (Bligh and Dyer \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1959\u003c/span\u003e). The extracted lipids were immediately subject to FAME analysis to minimize the likelihood of contamination and oxidation. The lipid-free tissue was recycled and lyophilized to constant weight for stable isotope analysis.\u003c/p\u003e\u003cp\u003eA modification of GAQSIQ method (GAQSIQ \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) was used to carry out the FAME analysis. Firstly, the extracted lipids were incubated with 4 mL of 0.5 mol/L KOH\u0026ndash;MeOH at 90 ℃ for 10 min, shaking for 5 s every 2 min. Then, 4 mL BF3/MeOH was added and incubated at 90 ℃ for 30 min, followed by the addition of 4 mL n-Hexane for 2 min incubation. Thirdly, 10 mL saturated NaCl was introduced for stratification at room temperature, and then the upper hexane layer containing FAME was transferred for analyses. FAMEs were determined using an Agilent 7890B gas chromatography coupled to a 5977A series mass spectrometer detector (Agilent Technologies, Inc. USA), equipped with a fused silica 60 m \u0026times; 0.25 nm open tubular column (HB-88: 0.20 \u0026micro;m, Agilent Technologies, Inc. USA). Individual FAME was identified by retention times and mass spectra with a known concentration internal standard 19:0 (GLC 37, Nu-Chek Prep, Inc.), using helium as the carrier gas, and a thermal gradient programmed from 125 to 250 ℃, with the auxiliary heater at 280 ℃.\u003c/p\u003e\u003cp\u003eThe amounts of individual fatty acids were expressed as percentages of the total FAs (%). A total of 37 fatty acids were detected, however, only 18 FAs (14:0, 15:0, 16:0, 17:0, 18:0, 16:1n7, 18:1n9c, 18:2n6, 18:3n3, 20:1, 20:2, 20:3n3, 20:3n6, 20:4n6, 20:5n3, 22:1n9, 22:2n6 and 22:6n3) constituted\u0026thinsp;\u0026gt;\u0026thinsp;0.2% of the total fatty acids, and were selected for statistical analyses following (Table S2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Energy density analysis\u003c/h2\u003e\u003cp\u003eEnergy density (ED) of muscle tissue was measured before extract lipids. An approximately 300 mg of powdered muscle tissue were used to determine the energy density (KJ/g), using an automatic isoperibol calorimeter (Model 6400, Parr Instrument Company, Moline, IL, USA). The calorimeter system was calibrated with the combustion of benzoic acid as the standard. Briefly, the powdered tissue was gently added to a capsule and then placed into the capsule holder of the calorimeter, which allows for automatically determining the energy density within 15 minutes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Stable isotope analysis\u003c/h2\u003e\u003cp\u003eAn approximately 0.3 mg lipid-free tissues were used to determine carbon and nitrogen stable isotopes. The powdered and lipid-free tissue was placed in a tin capsule and analyzed using a SerCon Integra 2 integrated elemental analyzer and an isotope ratio mass spectrometer (EA-IRMS) at the Stable Isotope Core Laboratory in Third Institute of Oceanography (Ministry of Natural Resources, China). The standard substance used for determining δ\u003csup\u003e13\u003c/sup\u003eC was Vienna Pee Dee Belemnite (V-PDB), and the standard substance used for determining δ\u003csup\u003e15\u003c/sup\u003eN was atmospheric nitrogen (N\u003csub\u003e2\u003c/sub\u003e). The values of δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN were determined using the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{{\\delta\\:}}^{13}\\text{C}/{{\\delta\\:}}^{15}\\text{N}=\\left(\\frac{{\\text{R}}_{\\text{s}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}}}{{\\text{R}}_{\\text{s}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}}}-1\\right)\\times\\:1000$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere R\u003csub\u003esample\u003c/sub\u003e and R\u003csub\u003estandard\u003c/sub\u003e represent the ratios of \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC and \u003csup\u003e15\u003c/sup\u003eN/\u003csup\u003e14\u003c/sup\u003eN of the sample and the standard reference material, respectively. Triplicate measurements indicated that the measurement errors were approximately 0.2\u0026permil; for δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e\u003cp\u003eSignificant differences were tested for the values of energy density, δ\u003csup\u003e15\u003c/sup\u003eN, δ\u003csup\u003e13\u003c/sup\u003eC and fatty acids between species. All data were checked for normality using a one-sample Kolmogorov-Smirnov test and for homogeneity of the variances using the Levene\u0026rsquo;s test. When the normality was satisfied, one-way analysis of variance (ANOVA) was applied to determine the significant difference between species, followed by Tukey\u0026rsquo;s honestly significant post-hoc test. When normal distribution and/or homoscedasticity were not achieved, data were subjected to a Kruskall-Wallis nonparametric test and a Games-Howell post hoc test was performed. A permutational ANOVA (PERMANOVA) with 10000 permutations was used to access the dissimilarity of the fatty acids composition among species. All analyses were performed using the base and vegan package (v. 2.6.8) in R (R Core Team \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The significant difference was considered when the \u003cem\u003ep\u003c/em\u003e value was \u0026lt;\u0026thinsp;0.05 for the statistical analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNiche metric estimation\u003c/b\u003e. The trophic niches for each species were estimated using the Stable Isotope Bayesian ellipses algorithm implemented in R (SIBER (v. 2.1.9), (Jackson et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)). The Bayesian approximation of the standard ellipse area (SEA\u003csub\u003eb\u003c/sub\u003e) and the standard ellipse area corrected by sample size (SEA\u003csub\u003ec\u003c/sub\u003e, an ellipse containing 40% of the data) was estimated based on 1,000 replications (Jackson et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). SIBER was also applied to estimate the nutritional niches (SEA\u003csub\u003eb\u003c/sub\u003e and SEA\u003csub\u003ec\u003c/sub\u003e) from the first two dimensions of a classical multidimensional scaling (MDS) applied to the 18 fatty acids. MDS is an algorithm suitable for pattern recognition (Delicado and Pach\u0026oacute;n-Garc\u0026iacute;a \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe SEA\u003csub\u003eb\u003c/sub\u003e were analyzed to test for the significant differences of niche space between species using ANOVA when the data satisfied the assumption of normality, and the Kruskall-Wallis nonparametric test when the normality assumption was rejected.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrophic breadth and dietary richness\u003c/b\u003e. The trophic breadth and dietary richness of each species was represented separately by the size of isotopic hypervolume and nutritional hypervolume, computed using Hutchinson\u0026rsquo;s \u003cem\u003en\u003c/em\u003e-dimensional hypervolume (Blonder \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hutchinson \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). The \u003cem\u003en\u003c/em\u003e-dimensional hypervolume highlights the importance of a multidimensional approach when studying ecosystem stability (Blonder et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stowasser et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and have been applied to uncover the individual environmental niches of white storks (\u003cem\u003eCiconia Ciconia\u003c/em\u003e) (Carlson et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and individual dietary specialization of squid(Lin et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The isotopic hypervolume and nutritional hypervolume for each species were estimated separately for stable isotopes and essential fatty acids using a Gaussian kernel density approach (Blonder \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The carbon and nitrogen stable isotopes were standardized using z-scores when estimating the isotopic hypervolumes; while the essential fatty acids were scaled using an MDS, with the first two dimensions of the MDS used to estimate the nutritional hypervolumes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBayesian linear hierarchical models.\u003c/b\u003e We employed Bayesian linear hierarchical models to investigate the relationship between energy density and trophic breadth or dietary richness. The model structure was specified as:\u003c/p\u003e\u003cp\u003eln (ED) = (\u003cem\u003eβ₀\u003c/em\u003e + \u003cem\u003eγ₀\u003c/em\u003e) + (\u003cem\u003eβ₁\u003c/em\u003e + \u003cem\u003eγ₁\u003c/em\u003e) X\u0026thinsp;+\u0026thinsp;\u003cem\u003eε\u003c/em\u003e\u003c/p\u003e\u003cp\u003ewhere ln (ED) is the natural log-transformed energy density, X is the z-score standardized trophic breadth or dietary richness, \u003cem\u003eβ₀\u003c/em\u003e is the fixed-effect intercept, \u003cem\u003eγ₀\u003c/em\u003e represents vectors of random-effect coefficients that account for residual intercept deviations attributable to species uniqueness, \u003cem\u003eβ₁\u003c/em\u003e is the fixed-effect slope for the standardized hypervolume (either trophic breadth or dietary richness), \u003cem\u003eγ₁\u003c/em\u003e denotes vector of random-effect coefficients that account for residual slope deviations attributable to species uniqueness, and \u003cem\u003eε\u003c/em\u003e is the model unexplained residual variation. This formulation allows for variation in both intercepts and slopes across species, accommodating heterogeneity in physiological or ecological constraints among taxa.\u003c/p\u003e\u003cp\u003eFurther, stable isotopes and fatty acids were used as predictors in Bayesian linear hierarchical models to examine the impact of feeding selection on energy density. To identify representative fatty acid predictors, a non-metric multidimensional scaling (NMDS) analysis was conducted on the standardized fatty acids variables using Bray-Curtis dissimilarity and vector fitting was applied to assess the relationship between each variable and the NMDS configuration (999 permutations, Spearman rank correlation). Only variables that exhibited both a statistically significant association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a high correlation with the NMDS axes (vector-fitting \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7) were retained.\u003c/p\u003e\u003cp\u003eThe posterior distributions of model parameters were estimated using Markov chain Monte Carlo (MCMC) methods using the \u0026ldquo;brms\u0026rdquo; R package version 2.22.0. Models were fitted using four MCMC chains of 15000 steps, including 7500-step warm-up periods, so a total of 30,000 steps were retained to estimate posterior distributions. Convergence was assessed using the Gelman-Rubin statistic (Rhat\u0026thinsp;\u0026lt;\u0026thinsp;1.01). This modeling approach accounts for non-independence among observations from the same species while estimating both average effects and interspecific variation in response.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Stable isotopes and isotopic niche\u003c/h2\u003e\u003cp\u003eThe 9 species exhibited δ\u003csup\u003e15\u003c/sup\u003eN values from 4.04\u0026permil; to 13.92\u0026permil; (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd, 9.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u0026permil;), and δ\u003csup\u003e13\u003c/sup\u003eC values from \u0026minus;\u0026thinsp;21.74\u0026permil; to -18.45\u0026permil; (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd, -19.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u0026permil;; Table S2). Both δ\u003csup\u003e15\u003c/sup\u003eN (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;365.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and δ\u003csup\u003e13\u003c/sup\u003eC (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;203.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) varied significantly among the 9 specie (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). \u003cem\u003eB. japonica\u003c/em\u003e and \u003cem\u003eG. borealis\u003c/em\u003e were determined the greatest value of δ\u003csup\u003e15\u003c/sup\u003eN values, whereas \u003cem\u003eS. melanostictus\u003c/em\u003e was determined the lowest δ\u003csup\u003e15\u003c/sup\u003eN values (Table S2). \u003cem\u003eE. luminosa\u003c/em\u003e and \u003cem\u003eO. bartramii\u003c/em\u003e were determined the greatest value of δ\u003csup\u003e13\u003c/sup\u003eC values, whereas \u003cem\u003eS. melanostictus\u003c/em\u003e was determined the lowest δ\u003csup\u003e13\u003c/sup\u003eC values (Table S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe isotopic niche analysis showed that some pairwise overlap was found for all species in the high seas area of northwest Pacific Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The Bayesian approximations of the standard ellipse area (SEA\u003csub\u003eb\u003c/sub\u003e) for the isotopic niches differed significantly between species (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;317.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The \u003cem\u003eS. japonicus\u003c/em\u003e showed the largest SEA\u003csub\u003eb\u003c/sub\u003e (2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27), followed by \u003cem\u003eS. melanostictus\u003c/em\u003e (2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26), \u003cem\u003eN. resplendens\u003c/em\u003e (1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26) and \u003cem\u003eS. californiensis\u003c/em\u003e (1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32), while \u003cem\u003eO. bartramii\u003c/em\u003e showed the smallest SEA\u003csub\u003eb\u003c/sub\u003e (0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Fatty acids and nutritional niche\u003c/h2\u003e\u003cp\u003eThere were significant differences in each individual fatty acid among the 9 species (Table S3). The overall FAs also differed significantly among species (PERMANOVA, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;64.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The first two MDS axes for the FAs data explained 68.58% and 13.14% of the overall variation for a total of 81.72% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The nutritional niche analysis showed that each squid species occupies a distinct and narrow nutritional niche, while some of the fish species overlap in nutritional niche (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The estimated SEA\u003csub\u003eb\u003c/sub\u003e values varied significantly among species (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;315.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The \u003cem\u003eS. melanostictus\u003c/em\u003e showed the largest SEA\u003csub\u003eb\u003c/sub\u003e, followed by \u003cem\u003eN. resplendens\u003c/em\u003e and \u003cem\u003eS. californiensis\u003c/em\u003e, while \u003cem\u003eG. borealis\u003c/em\u003e showed the smallest SEA\u003csub\u003eb\u003c/sub\u003e (0.0002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Table S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Energy density\u003c/h2\u003e\u003cp\u003eThe energy density values of the 9 species ranged from 17.80 kJ/g to 30.59 kJ/g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd, 21.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46 kJ/kg; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table S2). There were significant differences in the energy density among species (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;324.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eN. resplendens\u003c/em\u003e and \u003cem\u003eS. californiensis\u003c/em\u003e were determined the greatest value of energy density, whereas \u003cem\u003eE. luminosa\u003c/em\u003e, \u003cem\u003eG. borealis\u003c/em\u003e, \u003cem\u003eO. bartramii\u003c/em\u003e and \u003cem\u003eO. compacta\u003c/em\u003e showed the lowest energy density value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Energy density relation to trophic breadth and dietary richness\u003c/h2\u003e\u003cp\u003eThe trophic breadth, estimated using stable isotopes, ranged from 2.58 to 10.36 across the nine species (Table S5) and exhibited unsignificant positive allometric relationship with muscle energy density (average scaling exponent\u0026thinsp;=\u0026thinsp;0.09; 95% CIs \u0026minus;\u0026thinsp;0.10 to 0.29, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In contrast, dietary richness estimated by FAs varied from 0.001 to 0.10 across the 9 species (Table S5) and showed a significant positive allometric increase with muscle energy density (average scaling exponent\u0026thinsp;=\u0026thinsp;0.14; 95% CIs 0.02 to 0.25, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). There was a variation in the species-specific exponents derived from the model for nutritional hypervolumes, indicating that the relationship between nutritional hypervolume and energy density differs among species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, Table S6). \u003cem\u003eS. californiensis\u003c/em\u003e (0.21, 95% CIs 0.03 to 0.41) and \u003cem\u003eN. resplendens\u003c/em\u003e (0.21, 95% CIs 0.04 to 0.39) exhibited the highest exponent efficiency, while \u003cem\u003eE. luminosa\u003c/em\u003e (0.12, 95% Cis \u0026minus;\u0026thinsp;0.04 to 0.27) had the lowest value (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, Table S6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the wild, energy reserve is essential for survival of organisms throughout their life such as reproduction, migration and overwintering (Clarke \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pontzer and McGrosky \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Empirical evidence has showed that the energy accumulation of an organism is often constrained by both quantity and quality of food resources (Guo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jeanniard du Dot et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), with feeding habits playing a key role in mediating the transfer of external energy into internal allocation (Pontzer and McGrosky \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Prokopenko et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our results reveal significant interspecific variation in muscle energy density among pelagic nektons in the northwestern Pacific Ocean, with a positive allometric relationship between energy density and dietary richness. In contrast, no significant relationship was detected between energy density and trophic breadth. These findings support our second hypothesis that energy accumulation capacity is primarily regulated by the dietary richness, rather than by trophic breadth, providing robust empirical support for the pivotal role of feeding habits in shaping energy dynamics in marine predators.\u003c/p\u003e\u003cp\u003eThe framework of energy trade-offs under an economic perspective suggests that the energy flow through an organism is not constant, but is affected by the food availability (foraging efficiency), the capacity of energy absorption, and constraints on energy expenditure (Pontzer and McGrosky \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We observed a significant positive allometric relationship between muscle energy density and dietary richness among the nine species in the high seas area of the northwest Pacific. This pattern indicated that generalist species enhance energetic stability by broadening their dietary spectrum, thereby buffering against resource fluctuations and efficiently accumulating energy reserve (Garc\u0026iacute;a et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kohl et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For instance, \u003cem\u003eS. japonicus\u003c/em\u003e, \u003cem\u003eS. melanostictus\u003c/em\u003e, \u003cem\u003eN. resplendens\u003c/em\u003e and \u003cem\u003eS. californiensis\u003c/em\u003e, exploit a wide range of prey including plankton, small crustaceans and fish larvae (Hirai et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Watanabe et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), remains compensating for limited predatory capabilities through dietary flexibility. This strategy enables optimize nutrient acquisition and sustained energy reserve by consuming diverse prey assemblages (Pontzer and McGrosky \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such an approach is particularly advantageous under unstable food conditions, as energy reserves improves the ability to response intense predation pressure and fluctuations in food availability (Koemel et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The sufficient energy reserves also mitigate risks associated with environmental variability and support individual physiological condition (Peck et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), ultimately enhancing survival probability (Houston et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Thus, it would be not unexpected that generalist species have the large energy density (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and such pattern may be an adaptive trait for those species, consistent with the survival priority principle in ecological energetics (Sokolova et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tomlinson et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, feeding specialists such as \u003cem\u003eO. bartramii\u003c/em\u003e, \u003cem\u003eG. borealis\u003c/em\u003e, \u003cem\u003eO. compacta\u003c/em\u003e, \u003cem\u003eE. luminosa\u003c/em\u003e and \u003cem\u003eB. japonica\u003c/em\u003e, occupying narrow feeding niche and exhibiting relatively low energy densities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which may have evolved highly specialized foraging syndromes to maximize net energy gain per unit time. The apex species often rely on specific lipid-rich prey preys, such as orcas (\u003cem\u003eOrcinus orcas\u003c/em\u003e) consuming only the lipid-rich livers of sharks (Ford et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Mediterranean bogue (\u003cem\u003eBoops boops\u003c/em\u003e) target the lipid-rich gonads of mauve stinger jellyfish (\u003cem\u003ePelagia noctiluca\u003c/em\u003e) (Giacomo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While such diets yield high per-meal energy returns, they entail elevated hunting costs and metabolic rates (Prokopenko et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To offset energetic expenditures, these predators may increase feeding frequency, which leads to a high standard metabolic rate in active and relatively short fasting periods, resulting in tissue activity and physiological readiness (Bury \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, energy may be preferentially allocated to muscle development and locomotor performance to enhance hunting efficiency, rather than accumulate in muscle tissue. Furthermore, due to the extrinsic (resource availability) and intrinsic (physiological limits) constraints on energy flux, the amount of energy available for an organism at a given time is limited, and the allocation of energy must be directed to optimize its survival and offspring production (Sokolova \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In general, the basal maintenance is the high priority in the energy allocation to support the cellular and organismal homeostasis and carry out other fitness-related functions (Sokolova et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although the diet is rich in energetic contents, a significant portion of the energy intake would be preferentially allocate a substantial proportion of assimilated energy towards metabolic maintenance and physiological processes rather than energy reserve (Machovsky-Capuska and Raubenheimer \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Whitlock et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This characteristic thus may explain the energy accumulation capacity decreased with decreasing dietary richness for the nine pelagic species in the high seas area of northwest Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEnergy accumulation appears to emerge from synergistic interactions among diverse dietary components, rather than being driven primarily by the enrichment of specific prey types. Such phenomenon is supported by the results that no significant relationships were detected between individual fatty acid concentrations and energy density (Figure S3). This phenomenon likely stems from the functional differentiation of fatty acids within organisms (Tocher \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For example, saturated fatty acids are primarily incorporated into triacylglycerols for energy storage(Parrish \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), long-chain fatty acids play regulatory roles in lipid metabolism (Nakamura et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while essential fatty acid such as EPA and DHA are preferentially allocated to membrane fluidity, neural function and anti-inflammatory processes (Fuiman and Ojanguren \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Parrish \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given that the metabolic allocation of these fatty acids may prioritize physiological maintenance over energy storage, elevated fatty acid content does not inevitably lead to increased energy density. Therefore, the observed positive relationship between dietary richness and energy density likely reflects the comprehensive metabolic benefits of diversified nutrient intake, as a broader diet provides a more complete suite of metabolic precursors and co-substrates that enhance the efficiency of lipid biosynthesis pathways. This aligns with the geometric framework for nutrition, which posits that organisms optimize fitness not by maximizing single nutrients, but by achieving a balanced intake of multiple dietary components (Simpson and Raubenheimer \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis explanation of nutritionally synergistic interactions also provides a plausible explanation for the absence of significant correlations between stable isotope ratios (δ\u0026sup1;⁵N and δ\u0026sup1;\u0026sup3;C) or isotopic niche metrics and energy density (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Figure S2). Stable isotopes reflect long-term averages of basal carbon sources and trophic position (Layman et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Post \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), which poorly capture short-term variations in diet quality or the intake of specific nutritional components. In contrast, fatty acids serve as direct biochemical substrates for energy metabolism, structural biosynthesis, and signaling, offering a more sensitive and functionally informative proxy of recent diet quality (Iverson \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Parrish \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thus, it is dietary richness of dietary components rather than trophic breadth that more effectively determines energy accumulation capacity.\u003c/p\u003e\u003cp\u003eFurthermore, we observed interspecific variation in the strength of relationship between dietary richness and energy density, reflected in species-specific allometric scaling exponents (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). For example, \u003cem\u003eS. californiensis\u003c/em\u003e and \u003cem\u003eN. resplendens\u003c/em\u003e exhibited the steepest slopes, indicating high sensitivity of energy accumulation to dietary richness. In contrast, \u003cem\u003eE. luminosa\u003c/em\u003e showed the weakest relationship, suggesting greater reliance on specific lipid-rich prey or internal physiological regulation for energy reserve. This divergence likely reflects evolutionary adaptations to distinct ecological niches, such as migratory or seasonally breeding species (\u003cem\u003eI. argentinus\u003c/em\u003e) must rapidly accumulate energy to meet periodic demands during reproduction or migration (Lin et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the observed interspecific/inter-quality groups divergence in energy reserve allocation likely stems from niche specific foraging optimization driven by multidimensional variability in basal resource quality, given substantial differences in digestibility, and biochemical stoichiometry (Galloway et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Under evolutionary selection pressures, furthermore, consumers develop taxon-specific foraging syndromes to resolve the metabolic trade-off between energy efficiency and nutritional balancing. This trophodynamic optimization process directly modulates life-history trait expression through biochemical pathways linking nutrient intake (Potter et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results reveal a dynamic interplay between trophic breadth, dietary richness and energy accumulation capability for the nine pelagic nekton species in the high seas area of northwest Pacific. Consistent with our initial hypotheses that dietary richness is a key driver of energy accumulation in pelagic marine predators. The interspecific divergence in energy accumulation strategies may arise from adaptive feeding optimization. Generalist species achieve higher muscle energy density through a nutritionally diverse diet, enhancing energetic stability and resilience to environmental fluctuations. In contrast, despite often consuming energy-dense prey, specialists exhibit lower energy reserves, likely due to elevated metabolic demands and preferential allocation of energy toward maintenance and locomotor performance rather than energy reserve. Moreover, the absence of significant relationships between individual fatty acids and energy density implicates that energy accumulation influenced by the synergistic interactions among diverse dietary components. Interspecific variation in the scaling relationship further reflects evolutionary adaptations to distinct ecological strategies. Cumulatively, energy accumulation capability represents adaptive outcomes of feeding strategies, where biochemical and physiological optimizations mediate trade-offs in energy allocation, shaping both individual fitness and ecosystem-level energy transfer. Since ongoing change of climate has significantly influenced physiological and ecological aspects of marine organisms, future research should integrate long-term dietary monitoring with physiological assays to environmental drivers of energy allocation strategies. This study advances our understanding of trophodynamic regulation in marine ecosystems and emphasizes the importance of considering nutritional diversity in predicting organismal performance under changing ocean conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is a contribution of the Project on the Survey and Monitor-Evaluation of Global Fishery Resources (Comprehensive Scientific Survey of Fishery Resources at the High Seas), SHOU. We thank the staff members of the Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University for providing assistance at the laboratory. We also thank the undergraduates and crew onboard “SONGHANG” for assisting the samples collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNa Zang and Dongming Lin conceived the ideas and designed methodology; Na Zang and Yushuang Luo conducted the laboratory experiment; Na Zang and Dongming Lin analyzed the data; Na Zang wrote the manuscript; Dongming Lin, Bilin Liu and Xinjun Chen improved the manuscript. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (41876144), and Shanghai Talent Development Funding (2020107) to D.L., and National Natural Science Foundation of China (41876141) and Shanghai Science and Technology Innovation Program (19DZ1207502) to X.C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll specimens were analyzed in the laboratory using methods that are in line with current Chinese national standards, namely Laboratory Animals—General Requirements for Animal Experiment (GB/T 35823-2018). As all material sampled in this work was obtained from research vessel\u0026nbsp;“SONGHANG”\u0026nbsp;and the sample size of nekton collected for research purposes was kept to a minimum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm there are no competing interests or conflicts of interest with this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are available upon request. Requests for access the data should be directed to the corresponding author Dongming Lin at [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnthony, J. A., Roby, D. D. \u0026amp; Turco, K. R., 2000. Lipid content and energy density of forage fishes from the northern Gulf of Alaska. 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Ecological Indicators 158: 111563. https://doi.org/10.1016/j.ecolind.2024.111563.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"energy accumulation, feeding habits, dietary richness, trophic breadth, fatty acids, stable isotopes","lastPublishedDoi":"10.21203/rs.3.rs-7723242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7723242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnergy reserves are critical for survival, reproduction, and resilience to environmental variations for marine organisms. However, the energy accumulation capacity in terms of feeding habit impact remains poorly understood, especially for pelagic nektons in the high seas. Here, we measured muscle energy density, stable isotopes and fatty acids for nine pelagic species in the northwestern Pacific Ocean. We quantified isotopic hypervolume (based on stable isotopes, referred to trophic breadth) and nutritional hypervolume (based on fatty acids, referred to dietary richness) following Hutchinson\u0026rsquo;s \u003cem\u003en\u003c/em\u003e-dimensional hypervolume framework, and examined their relationships with energy density using Bayesian linear hierarchical models. Results revealed taxon-specific variations in energy accumulation capacity among the nektons. A significant positive allometric relationship was observed between energy density and dietary richness, whereas no significant association was detected with trophic breadth. Notably, energy density was not correlated with any individual fatty acid, suggesting that energy accumulation arises from synergistic interactions among diverse dietary components rather than specific prey item. Furthermore, interspecific variation in scaling exponents reflects niche-specific adaptations linked to life-history strategies. Our findings highlight that dietary richness shapes energy accumulation capability, emphasizing the importance of dietary complexity in mediating energy dynamics and species fitness in marine ecosystems.\u003c/p\u003e","manuscriptTitle":"Feeding habits affect the energy accumulation capability of pelagic nektons in the high seas of northwest Pacific","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 08:29:05","doi":"10.21203/rs.3.rs-7723242/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revise and Resubmit","date":"2026-04-27T08:12:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-03T14:51:11+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-16T14:56:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T09:56:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Biology","date":"2025-09-26T11:29:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6fbee6c3-73f3-4472-80e0-013e6ad00b01","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T12:17:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 08:29:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7723242","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7723242","identity":"rs-7723242","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}