Stable isotope values from organisms in the North Pacific Ocean: A reference for trophic ecology studies

Trophic ecology research provides insight into ecosystem form and function through an understanding of predator–prey dynamics (Boecklen et al., 2011). Analyses of the stable carbon (¹³C/¹²C; δ¹³C) and nitrogen (¹⁵N/¹⁴N; δ¹⁵N) isotope values from predator and prey tissues are a useful method by which to study foraging relationships. Stable isotope values increase with each trophic level due to consumer metabolism and the differential assimilation of heavier and lighter isotopes from prey into predator tissues (DeNiro & Epstein, 1978; Deniro & Epstein, 1981; Hobson, 1999). The differences in the stable isotope values between prey and predator tissues due to fractionation can be predictable and is measured as the trophic discrimination factor (TDF). TDFs are dependent upon several variables, including taxonomy (e.g., fish or mammal, etc.), consumer type (e.g., carnivore or herbivore), consumer sex (e.g., male or female), diet source (e.g., marine or terrestrial), and tissue type (e.g., blood or skin, etc.; Kurle, 2009; Kurle et al., 2014; Stephens et al., 2023).

Although TDFs would ideally be specific to the system being studied, controlled feeding experiments over months to identify TDFs are often prohibitively difficult. Generalized TDFs have been recognized (+1‰ for the δ¹³C values and + 3.4‰ for the δ¹⁵N values; Post, 2002) as a substitute when experimentally-derived TDFs are unavailable, but, when possible, it remains important to compare similar predator and prey tissues to control for the tissue-dependency of TDFs. Stable isotope values can be measured for soft tissues such as skin, blubber, and muscle, as well as hard tissues such as dentin and bone. The isotopic turnover, the rate at which stable isotopes in a tissue are replaced with those metabolized from the diet, varies by tissue type (Kurle, 2009). Blood has a relatively high turnover rate of weeks compared with the slow turnover rate of bone over years (Aurioles-Gamboa et al., 2013; Buchheister & Latour, 2010; Riofrío-Lazo & Aurioles-Gamboa, 2013). Therefore, similar tissues should be compared between predator and prey when possible.

Despite the challenges in standardizing isotope values and TDFs across taxa and tissue types, stable isotope analyses have been used to study trophic ecology at multiple temporal and spatial scales in the Pacific Ocean (among other marine and terrestrial environments). For example, Misarti et al. (2009) examined stable isotope values of bone collagen from fish and marine mammals in the Pacific Ocean over 4500 years, and Arnoldi et al. (2023) investigated stable isotope values from invertebrates and fish across the majority of the North Pacific Ocean basin. These studies demonstrate the utility of a multitaxa approach for understanding food webs and trophic dynamics.

To facilitate food web studies of diverse organisms within the North Pacific Ocean, we curated a database of stable isotope values from consumer species in six broad taxonomic groups: Cetacea (dolphins, porpoises, and whales), Pinnipedia (seals, sea lions, and walruses), Mustelidae (sea otters), Osteichthyes (bony fishes), Chondrichthyes (cartilaginous fishes), and Cephalopoda (squids and octopus). Our database (Table 1) is a comprehensive, but not exhaustive, overview of many ecologically important species of interest.

Our first goal was to capture the range of stable isotope values that could represent a species, given measurements from different time periods, regions, life stages, sexes, and tissues. We compiled 254 δ¹³C and δ¹⁵N values (with standard deviations, SD, or standard errors, SE) from 89 taxa across 74 published studies. Our second goal was twofold: to first condense the multitude of stable isotope values across studies into one representative range for that species (i.e., calculate one mean δ¹³C and δ¹⁵N value ± SD from values reported across multiple studies) and further condense the stable isotope values from multiple species into a representative range for that taxonomic group (i.e., calculate one mean δ¹³C and δ¹⁵N value ± SD to represent ‘pinnipeds’ from multiple species). Some studies could require the fine-scale data shown in Table 1, such as the δ¹³C and δ¹⁵N values for juvenile male Northern fur seals (Callorhinus ursinus), but others may aim to examine a broad taxonomic group, such as the δ¹³C and δ¹⁵N values for pinnipeds in the North Pacific.

To compute a single δ¹³C and δ¹⁵N value (± SD) from multiple studies and species (Table 2), we followed methodology in Carlisle et al. (2015). For example, we computed a single δ¹³C value (± SD) for the pantropical spotted dolphin (Stenella attenuata) by resampling 2000 values from a normal distribution with the parameters of Study 1 (mean δ¹³C value of −16.82‰ ± 0.69‰; Kanaji et al., 2017) and the parameters of Study 2 (mean δ¹³C value of −17.3‰ ± 0.3‰; Endo et al., 2010), then combining them into a cumulative distribution, and finally evaluating the mean δ¹³C and SD of the cumulative distribution (in this case, our reported δ¹³C value for pantropical spotted dolphins is −17.1‰ ± 0.6‰). We also resampled 2000 values from the δ¹³C and δ¹⁵N values ± SD of species with only one representative study to generate a distribution that incorporates the reported variation and remains consistent with our methodology for these generated δ¹³C and δ¹⁵N values.

We applied several filters and corrections to our compilation analysis of Table 1 to identify the single δ¹³C and δ¹⁵N values in Table 2. First, we excluded samples if they were collected from the Gulf of California due to bias from elevated δ¹⁵N values in this region (Altabet et al., 1999). Second, we aimed to compile the δ¹³C and δ¹⁵N values that were representative of soft tissues overall, with muscle specifically targeted when possible. We found muscle was the most frequently sampled tissue, allowing for greater consistency when compiling studies among a species. Soft tissues are also more likely to be useful for dietary analyses, as soft tissues are typically consumed by a predator and bones, teeth, or whiskers may be discarded. To this end, we chose muscle samples in cases of measurements from paired tissues in Table 1. We also corrected bone or teeth measurements to resemble muscle with the equation: δ¹³C_muscle = δ¹³C_bone/tooth – 2‰ (see supplementary material of Kim et al., 2012), except for sea otters (Enhydra lutris). The studies we report for sea otters sampled bone and whiskers, and the correction factor does not apply to whiskers. For consistency, we complied the hard tissue measurements (whiskers and bone) for sea otters as they were reported. Finally, if a study provided SE rather than SD, we converted SE to SD when calculating our study or species distributions (Table 1 reports the original SE values in such cases). We report single condensed stable isotope values (± SD) for each species in Table 2, and single condensed stable isotope values (± SD) for broad taxonomic groups in Table 3. All computations were conducted in R v. 4.2.1 (R Core Team 2022) using base functions.

Our reference database is informative for future researchers aiming to study trophic relationships within the North Pacific, variation in isotope values within species that may be due to differential life history traits, variation in isotope values across time, and the potential for differences in the trophic discrimination factors among multiple tissues from a species. It is worth emphasizing that we report the original data here to maximize adaptability, but studies that reference this database may consider accounting for or further examining tissue corrections, the Suess effect (Baxter & Walton, 1970), geographic variation (i.e., changes in the δ¹³C values with latitude or the elevated δ¹⁵N values of the Gulf of California; Ruiz-Cooley et al., 2012), or other appropriate measures for the context of their work.

Kelly R. Bowen: Conceptualization; data curation; formal analysis; investigation; methodology; writing – original draft; writing – review and editing. Carolyn M. Kurle: Conceptualization; resources; supervision; writing – review and editing.