A new era of isotope ecology: Nitrogen isotope ratio of amino acids as an approach for unraveling modern and ancient food web

Abstract

Food web research is rapidly expanding through study of natural fractional abundance of ¹⁵N in individual amino acids. This paper overviews the principles of this isotope approach, and from my perspective, reanalyzes applications, and further extends the discussion. It applies kinetic isotope effects that enriches ¹⁵N in certain amino acids associated with the metabolic processes, which was clearly demonstrated by observations of both natural ecosystem and laboratory experiments. In trophic processes ‘trophic amino acids’ such as glutamic acid that significantly enrich ¹⁵N, whereas ‘source amino acids’ such as phenylalanine and methionine show little ¹⁵N enrichment. Through various applications conducted over the years, the principles of the method have shown to operate well and disentangle complex food webs and relevant problems. Applications include food chain length estimate, nitrogen resource assessment, tracking fish migration, and reconstruction of paleodiet. With this approach, considerations of a wide range of classical issues have been reinvigorated, while in the same time, new challenging frontiers are emerging.

Introduction

Attempts to detect small fluctuations of the nitrogen isotope ratio (¹⁵N/¹⁴N) in natural amino acids date back to as early as 1930s, when applied isotope chemistry was still in its infancy.¹⁾ The pioneer study of Schenheimer and Rittenberg at Columbia University was followed by decades of efforts to isolate the individual amino acids from living organisms in order to determine their nitrogen isotope ratio.^2)–4) To precisely detect the very small isotopic variations mostly ranging from 0.36 to 0.39% as fractional abundance of ¹⁵N, it is a prerequisite that the amino acids are isolated from the samples with high degree of purity without modifying the isotope ratios. In 1980s to early 1990s a few laboratories overcame the technical difficulties, and outstanding progress was made that provided insights into microbial processes and the diet of fossil animals.^5)–11) Around 1990, a continuous-flow analytical system, ‘gas chromatography/combustion/isotope-ratio mass spectrometry (GC/C/IRMS)’ was newly developed and led to accelerate studies. Even though the isotopic compositions of all 20 amino acids cannot be determined due to the chromatographic limitations,^12),13) merits of this instrument are significant; both high-throughput and high sensitivity. Not surprisingly, this method soon became mainstream.¹⁴⁾

Meanwhile, natural-level stable nitrogen isotopic composition of whole body of living organisms contributed significantly as a natural tracer to investigate the food chains in a particular ecosystem. The phenomenon that ¹⁵N of animal body is enriched through the food chain was first recognized in late 1960s by E. Wada then at the Tokyo University of Education.¹⁵⁾ Subsequent studies revealed that the magnitude of ¹⁵N enrichment averages 3–4‰ per trophic step.^16)–19) With the knowledge that natural-abundance of carbon isotopic ratio (i.e., ¹³C/¹²C) that exhibits smaller elevation within food chains, carbon-nitrogen dual isotopic diagrams provide a useful tool for understanding and visualizing the trophic relationship among organisms in the ecosystem.^20)–23) Numerous applications of this approach have been made over the last several decades, and the field has come to be known as ‘isotope ecology’ or ‘stable isotope ecology’.^23),24) Already in the relatively early phase of the research on the isotope ecology, the applications further expanded to other related fields such as paleoanthropology and archaeology.^{8),11),20),25),26)} Considering the nitrogen in living organisms is derived mainly from amino acids, it would have been inevitable for isotope biogeochemists to focus on the ¹⁵N abundance of amino acids to further refine this approach.

As a successful integration of the above two research streams, McClelland and Montoya at Georgia Institute of Technology reported earlier this century that the ¹⁵N abundance of some amino acids like phenylalanine from marine zooplankton do not vary much with the trophic process, whereas other amino acids substantially enrich ¹⁵N relative to phytoplankton as their diet.²⁷⁾ The paper stimulated several groups including my group at JAMSTEC, where the use of nitrogen isotopic composition of amino acids began to be evaluated around 2005 by Y. Chikaraishi.²⁸⁾ It was followed by the proposition of an equation relating the nitrogen isotopic compositions of some amino acids and ‘trophic position’ based on the controlled feeding experiments of aquatic organisms.^29),30) We viewed such formulation as a crucial step on the way toward various applications of this novel biogeochemical tool. At about the same time, teams at least from University of California Santa Cruz, University of Hawaii, and Bristol University shared this research.^31)–33) This approach has spread and received attentions from a wide range of research fields such as ecology, fisheries, anthropology, and others as a potential tool for solving long-standing issues in their fields.³⁴⁾ It should be noted that in the same time, other research groups applied the same method to various topics.^35)–39)

Below, I describe the principles of this approach, and reanalyze applications related to the isotope ecology during the last two decades, and further extend the discussion. It is not my intention to summarize extensive literature in this growing field, but rather to aim to present a useful introduction of this topic. I also note that I arbitrarily select and focus on some specific topics with which I have been deeply involved, rather than covering the full spectrum of isotope ecology applications of this approach. The readers may find other recent reviews also useful.^34),39)

Principles

Amino acid metabolism and ¹⁵N.

In animals, amino acids are absorbed primarily as short peptides in the gut. After transport to the individual cell, the peptides are broken down into individual amino acids, joining the free amino acid pool (Fig. 1). The free amino acids in the pool are utilized for the synthesis of various proteins, but after a certain period of time, most proteins are broken down again to amino acids and returned to the pool. A portion of amino acids in the pool are constantly metabolized and undergo deamination (i.e., removal of amino group, -NH₂).^40),41)

Fig. 1.

A schematic view of major (but not all) metabolic processes of amino acids in an animal cell highlighted in nitrogen dynamics. Amino group of many amino acids are transferred to glutamic acid catalyzed by transaminase, and then the amino group of the glutamic acid is cleaved by glutamate dehydrogenase (GDH) and released as ammonia. The ammonia is excreted from the organisms as ammonia or urea. Note that both transaminases and GDH can catalyze the reverse reactions.

The deamination catalyzed by transaminase (or aminotransferase) is a common metabolic process for major amino acids to form the corresponding α-keto carboxylic acids (e.g., aspartic acid and α-oxaloacetate) as a half-reaction (Fig. 1). In contrast, the released amino group is transferred to α-ketoglutaric acid in the other half reaction to form glutamic acid that play a central role in amino acid metabolism. Some mechanistic details of this enzymatic reaction⁴²⁾ are known, and several studies have demonstrated that the entire reaction catalyzed by transaminase produces kinetically ¹⁵N-depleted glutamic acid.^4),43) The glutamic acid, one of the major amino acids composing the pool, is oxidatively deaminated to form ammonia and α-ketoglutaric acid catalyzed by glutamate dehydrogenase (GDH). We still know little of the exact magnitude of isotopic fractionation associated with the GDH deamination,⁴⁴⁾ but the overall processes kinetically produce somewhat ¹⁵N-depleted ammonia and leave ¹⁵N-enriched amino acids in the pool. The ammonia is excreted from the organisms as it is in most aquatic organisms like fish, whereas in amphibians and mammals it is excreted after transformation to urea. Loss of ¹⁵N-depleted nitrogen leaves organisms and amino acids enriched in ¹⁵N.

A simplified model of trophic isotopic fractionation.

Figure 2 shows a simplified dynamic model of the above amino acid metabolism, especially considering nitrogen isotope ratio. The size of the amino acid pool in vivo is considerably smaller than the protein reservoir, with typical example commonly found in metabolic systems.^45),46) The model assumes that amino acids brought into the pool from both the diet and protein reservoirs are mixed instantaneously. If zero-order kinetics is applied, the differential equations for the amino acid pool in the metabolic system are as follows:   

\begin{equation} \frac{\mathrm{d}[\text{AAs}]}{\mathrm{dt}} = f_{0} - f_{1}^{+} + f_{1}^{-} - f_{2}, \end{equation} [1]

  

\begin{align} \frac{\mathrm{d}[\text{AAs}]\delta_{d}}{\mathrm{dt}} &= f_{0}(\delta_{\text{d}} - \varepsilon_{0}) - f_{1}^{+}(\delta_{\text{AA}} - \varepsilon_{1}^{+}) \\ &\quad + f_{1}^{-}(\delta_{\text{p}} - \varepsilon_{1}^{-}) - f_{2}(\delta_{\text{AA}} - \varepsilon_{2}) \end{align} [2]

where f terms are fluxes, and (δ − ε) terms are isotopic values for fluxes. When considering a mature animal that the nitrogen mass balance and isotope balance are both achieved, above equations are described, respectively, as follows:   

\begin{equation} 0 = f_{0} - f_{1}^{+} + f_{1}^{-} - f_{2}, \end{equation} [3]

  

\begin{align} 0& = f_{0}(\delta_{\text{d}} - \varepsilon_{0}) - f_{1}^{+}(\delta_{\text{AA}} - \varepsilon_{1}^{+}) \\ &\quad + f_{1}^{-}(\delta_{\text{p}} - \varepsilon_{1}^{-}) - f_{2}(\delta_{\text{AA}} - \varepsilon_{2}). \end{align} [4]

In a mature animal, it is reasonably assumed that the nitrogen inflow (f₀) and outflow (f₂) are in equilibrium (i.e., zero growth rate). In this case, from Eq. [1], protein synthesis flux ($f_{1}^{ + }$) and degradation flux ($f_{1}^{ - }$) are also balanced as well.   

\begin{equation} f_{0} = f_{2}\ \textit{and}\ f_{1}^{+} = f_{1}^{-}(= f_{1}). \end{equation} [5]

Such a balance can be widely observed in healthy adult animals or human beings.^41),47) The isotopic fractionations associated with the peptide hydrolysis catalyzed by both protease and peptidase to individual amino acids (ε₀ and $\varepsilon_{1}^{ - }$) and the synthesis of protein ($\varepsilon_{1}^{ + }$) are assumed to be zero for simplification. To my knowledge, there is no literature that estimates at least $\varepsilon_{1}^{ + }$. In contrast, as some experimental studies have indicated, the amino acid metabolic step including transamination and deamination described above accompanies the significant isotopic fractionation (ε₂). The isotope balance Eq. [4] can be arranged to:   

\begin{equation} \frac{f_{1}}{f_{0}} = \frac{\delta_{\text{d}} - \delta_{\text{AA}} + \varepsilon_{2}}{\delta_{\text{AA}} - \delta_{\text{p}}}. \end{equation} [6]

In the cases of investigated healthy higher animals including human, $\frac{f_{1}}{f_{0}}$ ratio for the maintenance of proteins generally falls in a range 2 to 3.^{41),45),48)–50)} A considerable amount of amino acids released by protein catabolism is re-utilized for the synthesis of new proteins. Because in most animals the size of protein pool is 2 to 3 orders of magnitude larger than that of the amino acid temporal pool, δ_p can be regarded as the bulk isotope ratio of the whole organism. As numerous observations have indicated that the bulk organic matter of the predator is somewhat enriched in ¹⁵N relative to that of the diet (here the magnitude is referred to ‘TDF’ or trophic discrimination factor), we apply δ_p = δ_d + TDF to Eq. [6]. Then, the magnitude of ¹⁵N enrichment along the trophic process is given by:   

\begin{equation} \mathit{TDF} = \varepsilon_{2} + \left( 1 + \frac{f_{1}}{f_{0}} \right)(\delta_{\text{p}} - \delta_{\text{AA}}). \end{equation} [7]

Because it takes some time for the incorporated amino acids to be metabolized, the snapshot results should be treated in caution.^51)–55) If δ_p − δ_AA is sufficiently small, Eq. [7] implies:   

\begin{equation} \mathit{TDF} \approx \varepsilon_{2}. \end{equation} [8]

The Eq. [8] provides a theoretical background that when the growth rate is zero, TDF primarily reflects the magnitude of isotopic fractionation associated with the amino acid metabolism such as deamination. However, the Eq. [7] also suggests that in the fasting state (f₀ → 0), for example, TDF value fluctuates, which may also explain a part of variation of TDF value observed in natural environment.^{19),20),56),57)} The relationship between TDF and ε₂ needs to be carefully discussed.

Fig. 2.

The simple model for intake and metabolism of nitrogen by animals. ‘AA’ denotes amino acid pool in vivo. Nitrogen isotopic ratios of diet (d), temporal pool of amino acids (AA), protein reservoir (p), and excrement (ex) are denoted by δ_d, δ_AA, δ_p, and δ_ex, respectively. Flows of nitrogen denoted by f and isotope effects by ε. Isotopic ratios of nitrogen transferred the indicated pathways are given by δ_d − ε₀, $\delta_{\text{p}} - \varepsilon_{1}^{ - }$, $\delta_{\text{AA}} - \varepsilon_{1}^{ + }$, and δ_AA − ε₂.

Unfortunately, only a limited number of studies have reported the isotope effects associated with the metabolism of individual amino acids, making it difficult to rigorously verify the above considerations. Nevertheless, several studies have demonstrated that the transamination such as the synthesis of glutamic acid from aspartic acid results in an average isotopic fractionation of ∼6‰.^7),43) Although we do not know if this is representative value for isotopic fractionation of various transaminations and is a kinetic isotopic fractionation that does not necessarily have to match ε₂ in the model, the TDF value strongly reflects the ε₂ value, given that the amino acid pool is constantly being supplied by feeding and protein degradation. In practice, the TDF values range 2 to 5‰ (3.4‰ on average) in the various food webs and, $( 1 + \frac{f_{1}}{f_{0}} )$(δ_p − δ_AA) term in Eq. [7] is expected to vary negatively to some extent, probably −4‰ to −1‰. If the amino acid distribution of the prey is not significantly different from the predator (which warrants the constancy of ε₂), the δ_ex value should have a slightly (∼1 to 2‰) higher than δ_p, and both the $\frac{f_{1}}{f_{0}}$ and δ_ex terms regulate the TDF value.

The same scheme can be applied to explain the isotopic ratios of individual amino acids, the subject of this paper. It has been known that amino acid composition among various organisms (even among organisms at different trophic levels) are relatively similar (Fig. 3). Thus, the $\frac{f_{1}}{f_{0}}$ ratios of individual amino acids may not differ significantly as a whole. However, when we focus on individual organs, even within a single organism, as in the eye lens discussed below, there is diversity in amino acid composition and metabolic rate (i.e., varying $\frac{f_{1}}{f_{0}}$ ratio). Therefore, the TDF values for individual amino acids potentially differ among organs. In any case, the magnitude of ¹⁵N-enrichment of individual amino acids in the protein pools is not constant, governed primarily by ε₂ value and $( 1 + \frac{f_{1}}{f_{0}} )$(δ_p − δ_AA) term unique to each individual amino acid.

Fig. 3.

Hydrolyzable amino acid compositions as weight % of four types of aquatic organisms; a) natural phytoplankton (n = 4),¹⁶⁶⁾ b) natural zooplankton (n = 4),¹⁶⁶⁾ c) cultured bacteria (n = 3),¹⁶⁶⁾ and d) Atlantic salmon (n = 2).¹⁶⁷⁾ Glx and Asx indicate the sums of glutamic acid and glutamine, and aspartic acid and asparagine, respectively. n.d. denotes ‘no data’.

With another model, the dynamics of nitrogen isotope ratios in living organisms were carefully examined not only in mature stage (i.e., steady state), but also during growth and fasting stages (in section 4.7 of Ref. 23). Based on this study, it was demonstrated that δ_p increases during fasting (f₀ < f₂) and decreases during growth (f₀ > f₂). The magnitude of change in δ_p is also shown to be sensitive to the balance between f₀ and f₂. The fact that the magnitude of change in δ_p in ecological studies is consistent means that animal metablism in the food webs is regulated in highly reproducible manner by ingestion and excretion. Predation may control growth and reproductive patterns across animal taxa, forcing metabolic dynamics into a mode consistent with that seen in nitrogen isotope ratio.

Estimation of trophic position.

In the photoautotrophic cell as a start of the food chain, synthesis and degradation of 20 proteinaceous α-amino acids are balanced so that metabolism can take place normally. Under such a balance, it is one of the beauties of Nature or reflection of ‘isotopically ordered world’^58),59) that ¹⁵N is distributed among amino acids according to certain rules, which was practically confirmed by many studies. For photoautotrophs in the aquatic environment (i.e., primary producers like algae and cyanobacteria), for example, glutamic acid is 3 to 4‰ enriched in ¹⁵N relative to phenylalanine.^{27),29),60),61)} A similar regularity, albeit in different offset is observed in terrestrial photoautotrophs, in which glutamic acid is on average 8 to 9‰ depleted in ¹⁵N relative to the phenylalanine.^62)–64) Although the details of how such isotopic differences are to be explained are not yet known, these isotopic rules are principally independent of species or biota.

In terms of ¹⁵N enrichment magnitude along the trophic transfer, proteinaceous amino acids are practically classified as either ‘source’ or ‘trophic’ amino acids. The former group shows little ¹⁵N enrichment in trophic processes, whereas the latter substantially enriches ¹⁵N in the same process. Generally, phenylalanine and methionine and others such as lysine, tyrosine, and threonine are grouped into the source amino acids, and other most amino acids like glutamic acid, aspartic acid, alanine, valine, leucine, isoleucine and proline are grouped into the trophic amino acids. This grouping is, clearly, not determined simply by essential or nonessential designations that primarily reflect carbon relationships, but instead nitrogen metabolic pathways.⁶⁵⁾

Much discussion ensued based on various experimental results, for accurately estimating the trophic position. The idea is that the offset in nitrogen isotope ratios between trophic and source amino acids is strongly related to the trophic position (TP), a measure of the position it occupies in a food web. This has been generalized as Fig. 4 and the following equation^29),34);   

\begin{equation} \mathit{TP} = \frac{\delta^{15}\text{N}_{\text{trophic}} - \delta^{15}\text{N}_{\text{source}} - \beta}{\mathit{TDF}_{\text{trophic}} - \mathit{TDF}_{\text{source}}} + 1 \end{equation} [9]

where TDF and β represents, respectively the trophic discrimination factor and the δ¹⁵N offset between trophic and source amino acids from photoautotroph at the base of the food web described above. The weighted average of the TDFs of the 20 amino acids can be viewed as TDF as explained above. Trophic position is a quasi-continuous variable that can take decimal values, the basis of which is 1.0 for the primary producer and 2.0 for the primary consumers. The nitrogen isotopic ratio is expressed as conventional δ notation relative to the atmospheric N₂ or AIR standard (¹⁵N/¹⁴N ≃ 0.003677)⁶⁶⁾: δ¹⁵N ≡ [(R_sample/R_AIR) − 1] × 10³, where R is ¹⁵N/¹⁴N ratio. Units for δ are parts per thousand or permil (‰).

Fig. 4.

Schematic diagram showing the relationship between the δ¹⁵N offset of trophic and source amino acids and trophic position (TP). Typically, the trophic amino acid used in TP calculations is glutamic acid (Glu) and source amino acid is phenylalanine (Phe). Trophic discrimination factor (TDF) and β are shown graphically. Especially in the aquatic system, there are two scenarios have been proposed, and extreme cases are shown here. Scenario I indicates the case that TDF of trophic amino acids is constant through the food chain (TDF_Glu = 8.0‰, TDF_Phe = 0.4‰),²⁹⁾ whereas scenario II indicates that TDF of trophic amino acids decreases in high TP range (TDF_Glu = 6.4‰).³⁹⁾

Inasmuch as nitrogen isotopic variation is produced as a result of kinetic isotope effects, TDF and β should be, in theory, expressed as isotope fractionation or ratio of isotopic rate constants (i.e., ε) rather than the ‘difference of δ¹⁵N values’. However, numerically these two terms are very close, and thus in most cases there will not make any meaningful difference in the interpretation at this stage.

Thus far, δ¹⁵N values of glutamic acid and phenylalanine have been empirically recognized to be most valuable for assessing TP of organisms. Based on both field observations and diet-controlled culture experiments, it was proposed that the TDF_Glu is as large as 7 to 8‰ on average in particular for TP 1 to 2 and TP 2 to 3 (Fig. 4, Scenario I). Subsequent some studies confirmed the TDFs of trophic amino acids nearly constant between aquatic and terrestrial predation processes.^67)–69) In contrast, the other studies have suggested that the universality of the TDFs of trophic amino acids do not hold, but are apparently variable at least in aquatic food chains with some factors such as diet quality and mode of nitrogen excretion.^{39),59),70)–75)} Some studies have suggested that TDF_Glu in high TP organisms may be lower than the above values (7–8‰).^{39),60),70)–72)} For example, (TDF_Glu − TDF_Phe) value was estimated to be ∼5.7‰ based on a comparison of δ¹⁵N and gut contents in the teleosts across a wide range of TP (Fig. 4, Scenario II).⁶⁰⁾ Although the solution to this problem, which is difficult to know, is not straightforward, it is not surprising that TDF potentially fluctuates, since cellular amino acids form a strongly regulated dynamic system that are constantly synthesized and metabolized. Even among organisms at different trophic levels, amino acid compositions are relatively similar (Fig. 3), but minor compositional changes in AAs may be related to the TDF variability.^76),77) In practice, however, if the TDF_Glu is as small as ∼4‰ or lower as suggested by some studies,^39),69),70) organisms with extremely high TP such as TP = 6 or higher need to be assumed to explain the results presented in the following sections. In a ‘healthy population’ within a single species, the balance of amino acid metabolism would fall within a certain level, and thus the TDF values for the amino acids would also fall in a certain narrow range. If the metabolism fluctuates due to external or internal factors, the TDF is also expected to fluctuate.

In contrast to glutamic acid, experimentally determined TDF of phenylalanine as a source amino acid is close to zero. Phenylalanine is hydroxylated to form tyrosine as the first step in its metabolism, which does not accompany the cleavage of the carbon-nitrogen covalent bond.^28),77) Reasonably, TDF_Phe is quite small. Coupled with the fact that the phenylalanine is an essential amino acid for all animals, δ¹⁵N_Phe values even in higher-order predators are expected to be close to those of autotrophs such as algae or plants, which form the basis of the ecosystem. In the following discussion, to simplify the discussion, I assume the TDF values of glutamic acid and phenylalanine are 8.0‰ and 0.4‰, respectively, if not mentioned.

The essence of above equation can be graphically visualized in the δ¹⁵N_Phe-δ¹⁵N_Glu diagram (Fig. 5). Organisms belonging to a single trophic chain can be, in theory, plotted on a line roughly parallel to the vertical axis in this diagram, even if the TDF of glutamic acid varies along the food chain. In contrast, organisms with equivalent TP are plotted on a line parallel to the broken lines drawn from the lower left to the upper right in the diagram. Alike δ¹³C-δ¹⁵N diagram that has been applied classically, the horizontal axis of the δ¹⁵N_Phe-δ¹⁵N_Glu diagram indicates the source factor, whereas the vertical axis indicates the trophic factor. However, compared to the δ¹³C-δ¹⁵N diagram, an essential difference exists in this amino acid isotope approach. This recently developed approach is able to separate two major δ¹⁵N-controlling factors that originate from completely distinctive processes; environmental δ¹⁵N-baseline and trophic effect.^78),79)

Fig. 5.

δ¹⁵N_Phe-δ¹⁵N_Glu diagram for interpreting the food chains of aquatic and terrestrial food webs. Aquatic food chains are shown in blue and a terrestrial food chain in green. The diagonal dashed lines indicate trophoclines, or iso-TP lines. In this diagram, TP = (δ¹⁵N_Glu − δ¹⁵N_Phe − 3.4)/7.6 + 1 is applied to the aquatic food chain, and TP = (δ¹⁵N_Glu − δ¹⁵N_Phe − 8.4)/7.6 + 1 to the terrestrial food chain.^29),62) Three hypothetical food chains are shown in the diagram; Aquatic food chains 1 and 2 start at (δ¹⁵N_Phe, δ¹⁵N_Glu) = (4.0‰, 7.4‰) and (−3.0‰, 0.4‰), respectively, whereas terrestrial food chain starts at (δ¹⁵N_Phe, δ¹⁵N_Glu) = (11.0‰, 2.6‰). Hypothetical organism D feeds on TP 2 organism A belonging to aquatic food chain 1, and TP 3 organism B belonging to aquatic food chain 2, with a ratio of 1:1. Hypothetical organism E feeds on TP 2 organism A belonging to aquatic food chain 1, and TP 2 organism C belonging to terrestrial food chain, with a ratio of 7:3.

A few caveats of the approach.

A few caveats should be noted in this approach. First and foremost, ‘glutamic acid’ referred throughout this paper is a mixture of glutamic acid and glutamine, because glutamine is chemically deamidated to form glutamic acid in the process of amino acid extraction, or hydrolysis of peptide bonds. Currently, no efficient protocol is known to liberate glutamine from the peptides. These two amino acids are closely related metabolically, so that the nitrogen isotope ratios of the amino groups bonded to the α-carbons of glutamic acid and glutamine would be expected to have close values.

As a similar issue, what we call the nitrogen isotopic ratio of “amino acids” except glycine, is the weighted average of those of amino acid enantiomers.^80)–83) In the natural environment, d-amino acids are not only produced by racemization of l-amino acids, but are also abundantly synthesized by prokaryotes as peptidoglycan, a cell wall component. For alanine and glutamic acid, it has been demonstrated that d-enantiomers are up to a few permil depleted in ¹⁵N relative to the corresponding l-enantiomers in both cultured and natural organisms.^84)–86) If such case studies are widely true for other amino acids and in various environments, then the contribution of d-amino acids from microbes would lower the δ¹⁵N values of the amino acids to some extent. Lack of evidence hampers further discussion, but for the precise interpretation of isotopic records that strongly involved in microbial processes, this problem must be carefully evaluated in the future.

Secondly, the nitrogen isotopic composition of organic tissue reflects only that of the diet only absorbed and not all the contents found in the gut of organisms. Even though organic substances with high carbon-to-nitrogen ratio such as lipids and carbohydrates are digested and absorbed, they lack nitrogen and must have little effect on the TP defined above. The TP estimated through the nitrogen isotope ratio should not be strictly equivalent to the TP viewed in terms of carbon (or energy). Flows of carbon and nitrogen are decoupled not only in environmental processes but also in metabolic processes. This fact is recognized, for example, by considering the assimilations of carbon and nitrogen by autotrophs through separate mechanisms; For carbon, ambient CO₂ is fixed through enzymatic reaction catalyzed by rubisco, whereas for nitrogen, inorganic nitrogen such as NO₃⁻ and ammonia is fixed through enzymatic reaction catalyzed by either glutamine synthase or GDH. Furthermore, N₂ fixation and denitrification are highly important processes for the nitrogen cycle, but are not directly related to the carbon cycle. To be exact, the TP discussed here should be defined as ‘nitrogen-based TP’, and ‘carbon-based TP’ requires separate evidence and considerations. For example, in the case of humans, the main source of nitrogen is from land animals such as cattle and fish, so the nitrogen-based TP is 3 or higher. On the other hand, carbon is mainly consumed from grains such as rice and wheat, so the carbon-based TP is about 2.

Thirdly, care should be taken when selecting samples. It has been long known that individual organs and tissues even from a single organism have distinctive bulk isotopic signatures.^{26),54),87),88)} Such isotopic variabilities among organs and tissues even in a single organism may be ascribed mainly to the difference in metabolic rate (i.e., $\frac{f_{1}}{f_{0}}$ ratio in Eq. [7]) among organs⁸⁹⁾; muscle is constantly metabolized (i.e., produced and destroyed constantly), whereas organs such as bones, the eye lens and brain are in principal mostly accumulating amino acids and are metabolized much slowly. As described in the preceding section, even in the case of amino acids, differences in metabolic rates among organs and tissues result in differences in isotopic ratios.

Grazing food chain in the aquatic environment

A survey of the aquatic food web, especially in a confined space such as a lake, provides a good example to start with. The lake and its watershed form materially semi-enclosed system, and most organic matter produced there is consumed in the system. In other words, the organisms that inhabit that closed space are materially connected with prey-predator relationship. We have been studying three different types of lakes, Lake Superior, Lake Baikal, and Lake Biwa. Partial results were shown in elsewhere⁵⁴⁾ and studies of Lake Biwa and Lake Suwa are still on going.

Lake Superior.

Figure 6 shows the amino acid isotopic results from Lake Superior, one of the Great Lakes in northern North America, studies in collaboration with a research group at the University of Minnesota.⁹⁰⁾ We examined 8 animal species out of ∼80 species inhabiting this oligotrophic lake, which provided intriguing aspects as well as compared with the knowledge based on classical methods such as gut content analysis. Mean TP of Daphnia is ∼2.0, indicating this small zooplankton to be a typical herbivorous and feed only on photoautotrophs. The TP of small-sized copepod Diporeia is ∼2.4 on average, suggesting omnivorous with nearly half of its diet may depend on primary producers. The result of opossum shrimp Mysis (TP = 2.6 to 2.9) indicate somewhat more carnivorous than Diporeia, consistent with the previous observations.^91)–93) The copepod Limnocalanus macrurus registered at carnivorous TP 3.1 on average. In this way, herbivores, omnivores, and carnivores are clearly discriminated, and the degree of their carnivory would be estimated with some precision. The accuracy of the dietary evaluation is, in theory, dependent on the stability of TDF of glutamic acid and β value, but in application, it may also be dependent on the microbial activity, which will be discussed later.

Fig. 6.

Food web analysis of Lake Superior based on the nitrogen isotopic ratios of glutamic acid and phenylalanine. a) δ¹⁵N_Phe-δ¹⁵N_Glu diagram with trophoclines, and b) trophic position (TP) for 8 species collected from three sites (Bap, WM, and Ont) in Lake Superior. Of three sites, Bap and Ont located coastal, and WM offshore; Species analyzed are; zooplankton, Daphnia, Diporeia, opossum shrimp Mysis, copepod (Limnocalanus macrurus), lake herring (Coregonus artedi), kiyi (Coregonus kiyi), deep water sculpin (Myoxocephalus thompsonii), and lake trout (Salvelinus namaycush). The two axes for estimating TP are shown at the top and bottom of diagram b. The top axis corresponds to Scenario I shown in Fig. 4, with TDF_Glu set to 8.0‰,²⁹⁾ and the bottom axis corresponds to Scenario II, with TDF_Glu set to 6.4‰.³⁹⁾ In both cases, TDF_Phe was set to 0.4‰. The original data was reported and discussed in elsewhere.⁹⁰⁾

The estimated TPs of two small planktivorous coregonids, lake herring (Coregonus artedi) and kiyi (Coregonus kiyi) are both slightly higher than 3.0, suggesting them potential resource competitors. However, they appear to prefer different diets and are thought to be well segregated. The TP of benthic deep water sculpin (Myoxocephalus thompsonii) and lake trout (Salvelinus namaycush) are 3.8 and 4.0 on average, respectively, nearly one unit above the coregonids. The lake trout has been thought to be a top predator of the lake, whose stomach content analysis have suggested heavy reliance on other fish species, particularly deep water sculpin and coregonids.^93),94) The amino acid isotopic evidences support the interpretation of bulk isotopic results from Lake Ontario.⁹²⁾ Collectively, these observations revealed that the food chain in Lake Superior basically has four trophic steps. Accurate estimation of the TP would be helpful for understanding the food chain in detail, which potentially track persistent organic pollutants (POPs) and heavy metals intensively discussed in Great Lakes.⁹⁵⁾

δ¹⁵N_Phe values of above 8 species widely vary over 7‰ (−5‰ to 2‰), especially at coastal Bap and Ont sites (Fig. 6). It would reflect the seasonal changes in δ¹⁵N at the base of the food web associated with water dynamics, in addition to the existence of discrete ecosystems in coastal and offshore areas. Moreover, the contribution of allochthonous resources to the coastal areas may have an impact on the δ¹⁵N_Phe. Theoretically, such variabilities of δ¹⁵N_Phe can be particularly observed in low-TP organisms.^96),97) The food chain converges toward higher-order, long-lived predators, leading that the δ¹⁵N_Phe values of the predators also tend to focus in a certain range.

Lake Baikal.

An intriguing exercise is to compare the food web of Lake Superior (Fig. 6) with that of Lake Baikal (Fig. 7). Lake Baikal is the largest and deepest (∼1,642 m at maximum depth) freshwater lake on Earth and located in a similar latitudinal range. In this lake, many endemic species inhabit both coastal and offshore surface water, forming the characteristic ecosystem. In the offshore water, the ecological diversity is remarkably low with a limited number of species.⁹⁸⁾ The TP of mesozooplankton Episula baikalensis even collected in different years is 2.0 on average, suggesting them to depend entirely on photoautotrophs like Daphnia in Lake Superior. In contrast, microzooplankton gammarid Macrohectopus branikii occupies TP of ∼2.3 on average, suggesting omnivorous with about two thirds of its diet depend on primary producers. Benthic sculpin (Batrachocottus multiradiatus) registered clearly omnivorous at TP ∼2.7, which is a strong contrast with the deep water (DW) sculpin in Lake Superior whose TP obviously shows it carnivore. Commercially important pelagic whitefish omul (Coregonus autumnalis migratorius) has a mean TP of 3.6, which is consistent with behavior observations that its staple diet is the gammarid amphipod.⁹⁸⁾ Planktic sculpin (Comephorus dybowski) are higher order predators than omul, with an average TP of 4.3. Top predator of the lake is Baikal seal (Pusa sibirica) with TP 5.1 on average. In offshore ecosystem of Lake Baikal, the amino acid isotopic results showed that the TP of the top predator is about one unit longer than that estimated by δ¹⁵N_bulk (∼4.0),⁹⁹⁾ which might be the result of more variable TDF of bulk tissue.

Fig. 7.

Food web analysis of Lake Baikal based on the nitrogen isotopic ratios of glutamic acid and phenylalanine. a) δ¹⁵N_Phe-δ¹⁵N_Glu diagram with trophoclines, and b) trophic position (TP) for 7 species collected from offshore in Lake Baikal; phytoplankton (Melosira), zooplankton (Episula baikalensis), amphipod (Macrohectopus branikii), benthic sculpin (Batrachocottus multiradiatus), omul fish (Coregonus autumnalis migratorius), planktic sculpin (Comephorus dybowski), and seal (Pusa sibirica). The two axes for estimating TP are shown at the top and bottom of diagram b. The top axis corresponds to Scenario I shown in Fig. 4, with TDF_Glu set to 8.0‰,²⁹⁾ and the bottom axis corresponds to Scenario II, with TDF_Glu set to 6.4‰.³⁹⁾ In both cases, TDF_Phe was set to 0.4‰.

The food chain length (FCL) that corresponds to the TP of the top predator, is a central characteristic of ecological communities. Based on the amino acid isotopic results under the assumption of constant TDF through the food chain, the FCL are ∼4.0 for Lake Superior, ∼5.1 for Lake Baikal, ∼3.9 for Lake Biwa (Ishikawa, N. et al., unpublished results), and ∼3.0 for Lake Suwa (Urai, A. et al., unpublished results). In Lake Superior, the result appears to be consistent or somewhat shorter than those of previous studies.^100),101) If the TDF of glutamic acid were smaller than that assumed here (i.e., 8.0‰), the estimated FCL would be systematically longer than those described above. Any FCL estimates have specific assumptions inherent to a particular approach and should be carefully assessed. Nevertheless, the estimated FCL in the largest Lake Baikal (water volume: 23,600 km³) was significantly longer than the other three lakes, whereas it is by far the lowest in the smallest Lake Suwa (water volume: 0.063 km³), which does not contradict the hypothesis that ecosystem size determines FCL in lakes.¹⁰¹⁾ The inclusion of freshwater mammal seal in the Lake Baikal ecosystem is interpreted to push up the FCL nearly one unit from planktic sculpin.

Given all species comprising the ecosystem have the same opportunity to be preyed on their predators, ecosystem generates a triangle with the top-predator at the apex in the δ¹⁵N_Phe-δ¹⁵N_Glu diagram. It is theoretically anticipated that the δ¹⁵N_Phe of the high-TP organisms converges to a narrow range corresponding to averaged δ¹⁵N_Phe of biomass. The overall distribution of δ¹⁵N_Phe is −0.6 ± 1.9‰ (1σ, n = 30) for Lake Superior and 2.6 ± 1.9‰ (1σ, n = 53) for Lake Baikal. The ∼3‰ difference is attributed to the ¹⁵N-enriched nitrogen pool in Lake Baikal relative to Lake Superior due to geochemical processes such as ammonia evaporation or denitrification in the watershed.¹⁰²⁾ Together with the mean δ¹⁵N_Phe value of the top predator, the ecosystem triangle in the δ¹⁵N_Phe-δ¹⁵N_Glu diagram can be strictly defined by three components, including the FCL and the variation in δ¹⁵N_Phe of primary producers.

Feeding habit variabilities among closely related species.

How do dietary changes relate to other biological properties such as habitat, behavior, and even evolution? It often happens that similar organisms occupy different trophic niche, as seen in examples of deep-inhabiting sculpin in Lake Superior and Lake Baikal. It might be ascribed simply to ecological reasons such as the local extinction of predators or lack of food associated with the environmental change. Although sufficient evidence has not yet been accumulated, it appears that closely related species from different ecosystems often occupy distinct trophic niches. Here we compare 5 species of righteye flounder (albeit at different genus levels) that inhabit the soft muddy areas of the seafloor with similar behavioral patterns, collected at water depth of 220–501 m off the Pacific coast of northeast Japan.^95),103) Trophic positions of these species are roughly consistent among samples in the single species, but vary strikingly from 3.1 and 4.1 among species (Fig. 8). Of these, the lowest TP Rikuzen sole, Dexistes rikuzenius, may feed on detritus sinking from the overlying water column, whereas the highest TP flathead flounder, Hippoglossoides dubius, is clearly carnivorous. Such TP variations among righteye flounder species do not appear to be related to the size of specimens within a single species. Despite similar habitats and behavior, these righteye flounders feed on markedly different diet. In nature, feeding habit is not conservative and may be strongly regulated by several factors, including availability of the prey and competition with other species. Differences in species mean size might be related to food quality issues. Also, the dietary change is a potential driving force of sympatric speciation. Accumulation of the results would provide an insight into such an intriguing topic.

Fig. 8.

Summary of trophic position of 5 species of righteye flounder along the total length of the specimens; They are Dexistes rikuzenus (rikuzen sole), Clidoderma asperrimum (roughscale sole), Hippoglossoides dubius (flathead flounder), Glyptocephalus stelleri (blackfin flounder), and Microstomus achne (slime flounder). All these specimens were collected in the water depth range of 220–501 m from off Pacific coast of northeast Japan.⁹⁵⁾

Ecological consequences of the Anthropocene.

As partly discussed above, the δ¹⁵N_Phe value is informative for reconstructing the δ¹⁵N of the nitrogen pool in the system. It provides clues for understanding environmental issues such as the ecological consequence of eutrophication. In eutrophic lakes around the world, university laboratories and local museums often preserve diverse collections of formalin-fixed biological archives. Such biological archives potentially provide valuable samples for reconstructing the recent changes in lake environment and understanding its ecological consequence. The fixation by formalin (the aqueous solution of formaldehyde) has been one the most common fixatives used for the preservation of organisms. In the aqueous solution formaldehyde forms methanediol or methylene glycol, which is empirically known to react with the residues of some proteinaceous amino acids, including serine, threonine, glutamine, lysine, arginine, cysteine, and tyrosine to form reactive hydroxymethyl groups.¹⁰⁴⁾ The chemical process neither adds nor removes nitrogen from the protein (but adds carbon). Indeed, it was experimentally proven that the biological specimens fixed by formalin encode reliable nitrogen isotopic information in individual amino acids.^105),106)

This approach was applied to formalin-fixed specimens of gobiid fish (Isaza, Gymnogobius isaza, year 2nd), which were collected from Lake Biwa throughout the 20th century and stored in Center for Ecological Research, Kyoto University (Fig. 9). Based on the δ¹⁵N_bulk records of Isaza specimens as well as sediment collected from the lake, a gradual ¹⁵N enriching trend had been recognized, especially during the latter half of the 20th century when human activities seriously perturbed the lake environment.^107),108) For this temporal ¹⁵N enriching trend, two conflicting interpretations had been proposed; change in trophic niche (i.e., TP elevation) of Isaza along with the eutrophication, and elevation of δ¹⁵N of nitrogen pool. Given the elasticity of ecosystems, even in a single species TP would have a certain fluctuation depending on the environment. The results clearly demonstrated that the temporal change in the nitrogen isotopic composition was mostly attributable to that of nitrogen pool in the system.¹⁰⁵⁾ Through the 20th century, the estimated TP of Isaza remained nearly constant (∼3.2). In contrast, δ¹⁵N_Phe at the base of the food web (i.e., δ¹⁵N_base = δ¹⁵N_Phe − 0.4 × (TP − 1)) systematically increased from ∼5‰ in early 20th century to ∼9‰ in the latest 20th century (Fig. 9). Such a ∼4‰ elevation of δ¹⁵N_base during the 20th century has been explained by the reflection of enhanced denitrification along with the eutrophication of the lake.^109),110) Despite the deterioration of water quality and the almost simultaneous invasions of exotic species such as black bass and bluegill, trophic function of Isaza has remained largely unchanged. More formalin-fixed specimens of various species are currently being investigated.

Fig. 9.

A case study in Lake Biwa on the effects of lake eutrophication in the 20th century on the feeding habits of fish and the reconstruction of nitrogen cycles in the lake. a) Concentrations of nitrate (black) and phosphate (red) observed in the hypolimnetic water in the north basin of Lake Biwa,¹⁶⁸⁾ b) estimated trophic position of formalin-fixed gobiid fish Isaza (Gymnogobius isaza, Tanaka), and c) estimated δ¹⁵N_base, or δ¹⁵N_Phe value at the base of the food web (i.e., δ¹⁵N_base = δ¹⁵N_Phe − 0.4 × (TP − 1)).

Numerous lakes, ponds, and rivers in the world that have experienced or are presently experiencing the eutrophication like Lake Biwa. Studies of the formalin-fixed specimens archived around the world will provide unique but crucial information for discussing the ecological consequences of the Anthropocene.

Nitrogen resource apportionment between aquatic and terrestrial organic matters.

Besides glutamic acid and phenylalanine, the δ¹⁵N value of methionine (δ¹⁵N_Met), another source amino acid has been investigated its utility for the ecosystem study. As with phenylalanine, the metabolic processes of methionine start not with deamination, but with the methyl group removal to form homocysteine. Consequently, it is reasonably expected that the catabolism of methionine does not change its nitrogen isotope ratio. Taking advantage of the stability of the nitrogen isotope ratios of source amino acids along the food chain (∼0.4‰/TP for phenylalanine and ∼0.5‰/TP for methionine),²⁹⁾ an attempt was made to estimate the respective contributions of terrestrial and aquatic resources of nitrogen based on the δ¹⁵N offset between two source amino acids, phenylalanine and methionine (Fig. 10).¹¹¹⁾ By subtracting the nitrogen isotope ratios of the two source amino acids, the variation in the δ¹⁵N baseline can be canceled. The utility and accuracy of this estimate strongly depends on the robustness of the δ¹⁵N relationship between these two amino acids, although the relationship needs marginal corrections by TP. Our current evidence indicated as large as ∼12‰ isotopic offset (= δ¹⁵N_Met − δ¹⁵N_Phe) between aquatic and terrestrial ecosystems; the former is ∼−5‰, whereas the latter ∼−17‰ (Fig. 10). This could be successfully used to accurately assess TP in animals dependent on both terrestrial and aquatic resources by correcting for β values.^112),113)

Fig. 10.

Nitrogen resource apportionment (aquatic vs. terrestrial) based on nitrogen isotopic difference between phenylalanine and methionine (i.e., δ¹⁵N_Met − δ¹⁵N_Phe): From top to bottom, aquatic organisms, terrestrial organisms, and stream organisms that feed on both.¹¹¹⁾ At the bottom, the scale of terrestrial contribution (%) is shown.

The results of applying this approach to 8 species of animals including fish and insect larvae from a headwater stream food web are also shown in the bottom of Fig. 10, which are not contradicting with current knowledge on feeding behavior of these species.¹¹¹⁾ Of these, larvie of mayfly (Heptageniidae spp.) exhibits strong reliance (∼90%) on aquatic nitrogen resources. Nitrogen for the three fish species of goby (Rhindogobius flumineus), trout (Oncorhynchus masou ishikawae), and minnow (Rhynnchocypris oxycephalus jouyi) in the stream was found to be obtained roughly half from both the terrestrial and aquatic ecosystems. By focusing on protein resources, it allows to delve deeply into a classical topic for biogeochemistry, the interaction between distinctive terrestrial and aquatic ecosystems.^114),115) Research fields like paleoanthropology would benefit from the uniqueness of this approach. For example, the consumption of aquatic resources by inland hunter-gatherer communities has been thought to be strongly related to environmental adaptation, but it is challenging to assess accurately with classical approaches. This novel approach, if further confirmed, has the potential to contribute to even cultural issues.

Microbial processes and endosymbiosis

The outcomes of recent biogeochemistry research left little doubt about the fundamental role of microbes in the environment.^116),117) It is obviously necessary to know how such microbial processes affect the nitrogen isotope ratios of amino acids presented here. Interpretation of TP estimated by this approach often ignores microbial processes and may be somewhat less quantitative. When applied to natural ecosystems where degradation dominates, such as the deep sea, the effects of microbial activity must be carefully taken into account. In food web studies, the ‘detritus food chain’ has often been overlooked in comparison with ‘grazing food chain’, but these two are functionally interacted and not neatly separated.

Based on the laboratory culture experiments of microbial species including bacteria, archaea, and eukarya (fungi) in synthetic media aerobically,¹¹⁸⁾ the TP of microbes grown with free amino acid mixture (i.e., casamino acids) elevates one unit from their ‘diet’ (Fig. 11). In contrast, for the microbes grown with ammonium as the sole nitrogen source, δ¹⁵N of amino acid pattern including δ¹⁵N_Phe-δ¹⁵N_Glu relationship is very similar to that of algae with TP of 1.^118),119) For heterotrophic microbes including bacteria and fungi in the terrestrial environment, the amino acid approach was also demonstrated to be effective.¹²⁰⁾ These observations may reflect the fact that amino acid metabolism in microorganisms is essentially the same as in other organisms, including photoautotrophs. An ecological perspective is that the apparent TP of detritivores that consume microbe-rich organic matter would be not simply one unit higher that of the macroscopic ‘prey’, but even higher. The TP values found in deep-sea organisms may represent an example of microbial links in the natural food chain. The observed TP of deep sea (200–550 m water depth) fish off Japan coast starts at TP ∼3.2.⁹⁵⁾ On the other hand, if a mixture of phytoplankton (TP 1) and zooplankton (TP 2) biomasses directly sinks to the deep ocean and is fed by the deep-sea fish, their TP should theoretically start at ∼2.5. The difference between these observational and theoretical values is explained potentially by the microbial links in the water column.

Fig. 11.

The nitrogen isotopic difference between glutamic acid and phenylalanine (i.e., δ¹⁵N_Glu − δ¹⁵N_Phe) from various microbes cultured in the laboratory.¹¹⁸⁾ Culture experiments were conducted by both grown with ammonia (closed symbols) and with casamino acids, a mixture of amino acids (open symbols). Because δ¹⁵N_Glu and δ¹⁵N_Phe values of the casamino acids are 10.2‰ and 5.8‰, respectively, the (δ¹⁵N_Glu − δ¹⁵N_Phe) value of microbes is expected to be 12.0‰ (dotted line) when they assimilate casamino acids. Broken line indicates the expected value (δ¹⁵N_Glu − δ¹⁵N_Phe = 3.4‰) when the microbes grow using free NH₃. Cultured microbes are selected from major three domains of organisms. Bacteraia: Escherichia coli, Archaea: Methanothermobacter thermautotrophicus, Sulfolobus tokodaii, Halobacterium salinarum, and Eukarya: Saccharomyces cerevisiae. Details of cultured conditions were described in elsewhere.¹¹⁸⁾

An interesting application of this knowledge is host-symbiont nutritional relationship in endosymbiosis observed widely in natural environment. So far, the nutritional relationship between microbial symbionts and hosts such as foraminifera, sea slugs, and deep-sea mussel have been investigated (Tame, A. et al., unpublished results).^121)–124) In all these cases, in favorable environments, microbes grown with inorganic nitrogen (i.e., TP = 1) as symbionts supply nutrition (i.e., amino acids) they produced as photosynthates to the hosts. The host is supported by the amino acids produced by the symbionts as well as by its own food harvesting, and its trophic position takes values between 1 and 2. In contrast, in unfavorable environment such as shortage of food, the hosts directly feed on the symbionts, and TP of the hosts substantially increases up to 2. These studies clearly demonstrated that the hosts switch the nitrogen source between being supplied by the symbiont and consuming the symbiont, dependent on the environment. Amino acid isotopic approach provides insight into the strategies of the hosts for survival in a fluctuating environment.

Tracking migration routes of marine fish

Since many marine fishes that are important as fishery resources are migratory, knowing the details of their migration is essential for fisheries management and marine conservation. Isoscape, which maps isotopes, is a relatively recent approach for potentially contributing to this issue.^125)–128) A utility of δ¹⁵N_Phe that reflects the base of the food chain is its application to marine biological samples through isoscape for tracking the migration of fish in time and space.¹²⁹⁾ This approach is clearly demonstrated to be helpful for reconstructing the long-distance migration route of chum salmon (Oncorhynchus keta) in the northern North Pacific.¹³⁰⁾ The δ¹⁵N_Phe as well as δ¹⁵N_bulk recorded in the growth ring of vertebral centra^131),132) of chum salmon collected from a Japanese river where they migrated to spawn was integrated with δ¹⁵N isoscape in the northern North Pacific through a statistic model. The reconstructed migration route of chum salmon reproduced the known migration pattern^133),134) with high spatial and temporal resolutions relative to previous studies with classical approaches (Fig. 12). After roaming the northwest Pacific Ocean for a year or two, the chum salmon gather at the later stage of their growth on the continental shelf in the eastern Bering Sea where they mature supported by high productivity. Coupling with the isoscape, amino acid isotope approach acquires versatility, potentially being applicable to most fish for tracking of their migration routes. Simultaneously, its drawback is that the amino acids are only minor components in the vertebral centra, limiting the sample amount, particularly for juvenile period and that after growth slows down due to sexual maturity.

Fig. 12.

The migration areas of chum salmon (Oncorhynchus keta) along growth stages (A–D) were estimated by matching δ¹⁵N_Phe and δ¹⁵N_bulk recorded in centrifugal vertebral growth rings to the North Pacific δ¹⁵N isoscapes using a state-space model.¹³⁰⁾ The color gradients indicate presence probability of chum salmon in different growth stages. The δ¹⁵N isoscape was constructed with the analytical results of long-lived copepod.

Recently, eye lens has demonstrated as a potential archive for circumventing this issue.^135)–137) The eye lens composed mainly of proteins (i.e., amino acids), supply a plenty of sample and more suitable for this kind of study compared to the vertebral centra or otolith, even though its growth rate also slows down in the later life stage. The eye lens forms successive layers of cortical laminae, which can be manually separated layer by layer. We have successfully separated as many as 34 layers from a single eye lens of ∼5 mm i.d. from chub mackerel (Scomber japonicus).¹³⁷⁾ The amino acid isotopic results of some of these samples showed lifelong changes in the δ¹⁵N of baseline. In particular, early life histories of the fish is densely recorded, allowing to reconstruct the diet for juvenile, which is often important for conservation and aquaculture of the species.¹³⁸⁾

The amino acids that form the eye lenses of all animals are little metabolized after accumulation. Our knowledge on the growth rate of the eyeball and metabolism of amino acids forming the eye lens still needs to be accumulated, but this method is potentially applicable not only to the marine organisms but also to the terrestrial animals and may be useful for reconstructing the detailed behavior analysis of a variety of organisms in the future.

Applications to paleodietary study

Another exciting application of the compound-specific isotopic approach is paleodiet reconstruction using mammalian bone collagen, a triple helix made of amino acid sequence.^{8),139)–141)} Since the early phase of the development of our investigation of this approach, various fossil bones of vertebrates from archaeological sites have been studied with the collaboration mainly with a research group at The University of Tokyo.

Once put aside its applications, the utility of the amino acid isotope approach to the archaeological studies can be partially validated by bone collagen samples from herbivorous animals which are reasonably expected to have TP of 2.0. Figure 13 summarizes the all available analytical results of bone collagens of various herbivores collected from archaeological sites in Japan, Syria, and France (3,000 to 8,000 years BP).^142)–149) Most plots (n = 51) fall along the trophocline of TP 2 (average: 2.0, standard deviation: 0.1), confirming that the method is effective for reconstructing at least herbivore TP using fossil bone collagen. Exceptions are results of deer and cattle from Kitakogane shell midden and Moyoro site (n = 6), both from coastal area of northern Japan with cold and snowy climate.¹⁵⁰⁾ These samples exhibit somewhat high TP ∼2.4 with relatively ¹⁵N-depleted phenylalanine. From the perspective of a top-down approach,¹⁵¹⁾ these results are potentially explained by either microbial contribution in their diet or the consumption of marine-derived resources (δ¹⁵N_Phe ranges 1 to 2‰ in these sites)¹⁴²⁾ such as seaweeds, as sometimes observed in cold regions when available food is limited for terrestrial herbivores in winter.¹⁵²⁾

Fig. 13.

δ¹⁵N_Phe-δ¹⁵N_Glu diagram for fossil bone collagen from herbivores collected from archaeological sites. Trophoclines in the diagram is drawn with 7.6‰ for (TDF_Glu − TDF_Phe) value and −8.4‰ for β value. The archaeological sites include Kitakogane shell midden,¹⁴²⁾ Moyoro site,¹⁴³⁾ Kitamura and Tochibara sites,¹⁵⁴⁾ Tell-el-Kerkh,^146),147) and Noyen-sur-Sein.¹⁴⁹⁾ Plots enclosed by a broken circle are samples from Kitakogane shell midden and Moyoro site.

In the bulk isotope approach applied for decades, the ‘collagen’ samples with carbon-to-nitrogen (C/N) ratio outside of a certain range (e.g., 2.5–3.1 as weight ratio)¹⁵³⁾ have been interpreted to be ‘diagenetically altered’, and thus intentionally removed from the discussion. In the amino acid isotope approach, however, such a strict operational distinction may not be required. From both physicochemical and empirical perspectives, hydration rates (i.e., cleavage rate of peptide bond) in diagenetic processes of bone collagen are different among individual amino acids, modifying amino acid composition and thus δ¹⁵N of bulk collagen as diagenesis proceeds. Since δ¹⁵N values of individual amino acids widely vary especially for high-TP organisms, amino acid compositional change through diagenesis should substantially alter the δ¹⁵N_bulk record. The increase in the C/N ratio of the bulk collagen in the diagenetic samples may be primarily due to the removal of amino groups from the N-terminus of the peptide and amino acid residues (i.e., glutamine to glutamic acid, asparagine to aspartic acid, and arginine to glutamic acid).⁷⁷⁾ Also, the effect of contamination of exogenous amino acids cannot be entirely ruled out. In the future, some criteria for assessing the nature of amino acids from fossil bone would be required.

Because the amino acid approach focuses on nitrogen composing the protein backbone which is strongly connected with carbons for both sides, in theory, it is not exchangeable with ambient nitrogen such as ammonium during diagenesis. Unless exogenous amino acids are added secondarily, changes in amino acid composition during diagenesis do not alter the δ¹⁵N record of individual amino acids. This consideration is empirically supported by data shown in Fig. 14, which is a replot of the data previously reported.¹⁵⁴⁾ Both δ¹⁵N_Glu and δ¹⁵N_Phe values of collagen from human fossil bone, and hence estimated TP little vary (TP range: 2.6–2.8) even in the collagens with bulk isotope ratios of ∼4‰ variation and with C/N weight ratio up to ∼9. The observation does not necessarily guarantee the reliability of this approach, but it certainly warrants further study, including consistency with experimental observations that chemical hydrolysis of peptides at high temperatures leads to fractionation of nitrogen isotopes.^155),156)

Fig. 14.

Evaluation of the diagenetic effects on nitrogen isotope ratios of amino acids. The samples shown here were collagens extracted from human bone samples collected at inland Tochibara and Kitamura sites, Japan (original data from Ref. 154). In the upper panel, estimated TP is plotted as a function of C/N (weight) ratio of the fossil collagen samples extracted from human bones. In the lower panel, nitrogen isotopic compositions of both glutamic acid and phenylalanine are plotted against C/N ratio of the collagen. The grey zone indicates the normal acceptable range of C/N weight ratio (2.5–3.1) in the bulk isotopic analysis.¹⁵⁴⁾

Logically, the δ¹⁵N value of bulk collagen would strongly reflect that of glycine, which accounts for about one-third of amino acids in bone collagen. However, glycine metabolic pathways are multiple and complex, so the δ¹⁵N of glycine, and thus of bulk collagen is not explicitly related only to trophic processes,¹⁴⁶⁾ but could be regulated to some extent by the balance of the multiple metabolic pathways related to glycine.

The estimated TP of paleo-human from inland of Japan and Europe who are considered to little feed on aquatic resources range 2.2 to 2.9. Among them, Neanderthals from Spy Cave showed the higher TP compared to Homo sapiens, ranging from 2.7 to 2.9,¹⁴⁵⁾ supporting the previous thoughts of being primarily carnivorous.¹⁵⁷⁾

Bridging bulk and amino acid approaches

In this section, I will consider how to connect the traditional bulk isotope data with the amino acid approach. Nitrogen isotopic ratio of bulk organism can be expressed as the weighted mean of various nitrogenous compounds composing the organism;   

\begin{equation} \delta_{\text{bulk}} = g_{1}\delta_{1} + g_{2}\delta_{2} + g_{3}\delta_{3} + \ldots + g_{\text{n}}\delta_{\text{n}}, \end{equation} [10]

where g represents the nitrogen fraction of the individual nitrogenous compounds in the total nitrogen, satisfying g₁ + g₂ + g₃ + … + g_n = 1. The subscripts 1 through n denote the respective nitrogenous compounds. The same can be applied to the TDF;   

\begin{align} \mathit{TDF}_{\text{bulk}} &= g_{1}\,\mathit{TDF}_{1} + g_{2}\,\mathit{TDF}_{2} \\ &\quad + g_{3}\,\mathit{TDF}_{3} + \ldots + g_{\text{n}}\,\mathit{TDF}_{\text{n}}. \end{align} [11]

The TDFs of individual amino acids appear to fall within certain ranges, especially in the lower trophic levels. Also, if the amino acid composition is relatively constant throughout the organisms (Fig. 3), it is reasonable to assume a relationship between TDF_bulk and the mean TDF of amino acids. Figure 15 indicates the mean TDF at the lower TP organisms (i.e., not teleost) in the aquatic realm.^{29),68),120),158)} Although the TDF values of cysteine, histidine, arginine, and tryptophan are not known, weighted mean of TDF values is ∼4.2‰ for these amino acids which account for ∼85% of nitrogen of proteinaceous amino acids. This value is significantly (∼0.8‰) greater than the known mean TDF_bulk (∼3.4‰).^19),22),59) The large mean TDF observed in amino acids must be offset somewhat by nitrogenous compounds with smaller TDF. The four amino acids and the amide nitrogen of glutamine and asparagine, which cannot be measured for analytical reasons, are not taken into account in the above calculations and may contribute to this difference. On the other hand, other nitrogen compounds such as nucleobases need to be taken into account. Quantitatively, proteinaceous amino acids are the major source of nitrogen in the organisms, accounting for 80–90% of biological nitrogen, whereas nitrogen from nucleobases (i.e., adenine, guanine, thymine, cytosine, and uracil) and other nitrogenous compounds roughly account for ∼10% and <5%, respectively.^159),160)

Fig. 15.

The TDF (trophic discrimination factor) of individual amino acids for the lower TP (1 to 3) organisms in the aquatic environment (n.d.: no data).^{29),68),120),158)} Weighted mean TDF is calculated based on the averaged distribution of amino acids shown in Fig. 3. TDF values of cysteine, histidine, arginine, and tryptophan are unknown and therefore not included in this calculation.

Like many proteinaceous amino acids, compounds with nitrogen as an amino group are potentially enriched for ¹⁵N by deamination. This means that they may have certain magnitudes of TDF. Nucleobases are principally included in the category of such compounds. Although there are only few reports on the nitrogen isotope ratios of nucleobases, those of the five major nucleobases from mammalian DNA rather suggest that the average TDF of the nucleobases is substantially (∼5‰) negative.¹⁶¹⁾ The nucleobases may explain an important part of the difference between the amino acid TDF and TDF_bulk. Furthermore, compounds that are primarily supplied through the diet and do not involve nitrogen in the metabolic process (in the same category as the source amino acids) would be expected to lower TDF_bulk. Hemes and nitrogenous lipids are probably the compounds in this category.¹⁶²⁾ A recent analysis of heme B, the most abundant heme species in the natural environment, suggested that the TDF of this compound may be relatively small.¹⁶³⁾ The gap between the two approaches, amino acids and bulk, should narrow as more such data become available.

Conclusions

It is very significant that macroscale processes such as predation can be measured using ¹⁵N abundance of simple compounds that are regulated by statistical thermodynamics and physiology. However, it is obvious that much still remains to be done. As pointed out by the previous summary paper,³⁴⁾ accurate knowledge of TDF especially for higher-TP organisms is still lacking and, as is accurate knowledge of the isotope effects associated with the amino acid metabolism behind it. Even though the method still needs further evaluation, more amino acid isotope ratios would advance food web research, given that observation-based studies lag far behind theoretical studies in this field.^164),165)

In this paper, I have focused on the nitrogen isotopic ratios of only three amino acids, glutamic acid, phenylalanine, and methionine, but in the future the isotopic information encoded in other amino acids may also be explored in depth for ecological applications. Although it has not been much of an issue among geochemists, knowledge of the mechanistic details of enzymatic reactions such as deamination and peptide cleavage will eventually be necessary to understand the magnitude of nitrogen isotope fractionation associated with amino acid metabolism.

Isotope ecology has been established and developed by isotopic compositions of bulk samples that accumulated over the last many decades. The bulk approach has the advantage over the amino acid approach providing a systematic perspective when the organism is considered as a box with diverse functions. Bulk information is something that can never be obtained by probing deeply into a single compound or compound group. However, at present the results of the bulk approach do not appear to be very well coupled with those of the amino acid isotopic approach which has been developed rapidly in recent years, as presented in this paper. Further growth of the amino acid approach is needed to link the two more systematically.

Acknowledgments

I am deeply indebted to E. Wada for encouraging me and my group, to pursue this topic over the decade. In writing this review, I would like to thank the three anonymous reviewers and E. Wada for constructive comments. In conducting the research presented here, I sincerely thank my marvelous colleagues working for many years, especially for Y. Chikaraishi, N. O. Ogawa, and Y. Takano who have enormously contributed what I described in this paper. I also wish to thank my excellent colleagues and collaborators around the world who have applied the method to various fields of science; They are T. Nagata, K. Maki, A. Wyatt, A. Kohzu, Y. Yamaguchi, Y. Takizawa, N.F. Ishikawa, H. Nomaki, M. Tsuchiya, K. Fujita, Y. Naito, N. Honch, Y. Itahashi, M. Yoneda, L. Rey, G. Goude, J. Matsubayashi, C. Yoshikawa, Y. Harada, I. Tayasu, C. Yoshimizu, K. Koba, T. Tsutaya, M. Nakabayashi, S.A. Steffan, B.R. Kruger, E.C. Minor, C. Campbell, D.M. Nelson, Q.H.S. Chan, J.T. Brenna, S. Miyachi, M.J. Miller, K. Tsukamoto, Y. Kashiyama, M. Ishikawa, A.S. Goto, S. Furota, T. Furota, K. Fujikura, Y. Fujiwara, A. Tame, T. Yoshida, K.H. Shin, H. Suga, H. Kitazato, N. Yoshida, N. Okada, Y. Miyake, B. Thibodeau, Y. Yokoyama, A. Urai, Y. Sun, Y. Isaji, and Y. Sasaki.

Notes

Edited by Eitaro WADA, M.J.A.

Correspondence should be addressed to: N. Ohkouchi, Biogeochemistry Research Center, Japan Agency for Marine-Earth Science and Technology, 2-15 Natsushima-cho, Yokosuka, Kanagawa 237-0061, Japan (e-mail: [e-mail]).

References

• 1)   Schoenheimer,  R. and  Rittenberg,  D. (1939) Studies in protein metabolism I. General considerations in the application of isotopes to the study of protein metabolism. The normal abundance of nitrogen isotopes in amino acids. J. Biol. Chem. 127, 285–290.
• 2)   Abelson,  P.H. and  Hoering,  T.C. (1961) Carbon isotope fractionation in formation of amino acids by photosynthetic organisms. Proc. Natl. Acad. Sci. U.S.A. 47, 623–632.
• 3)   Gaebler,  O.H.,  Choitz,  H.C.,  Vitti,  T.G. and  Vukmirovich,  R. (1963) Significance of N¹⁵ excess in nitrogenous compounds of biological origin. Can. J. Biochem. Physiol. 41, 1089–1097.
• 4)   Gaebler,  O.H.,  Vitti,  T.G. and  Vukmirovich,  R. (1966) Isotope effects in metabolism of ¹⁴N and ¹⁵N from unlabeled dietary proteins. Can. J. Biochem. Physiol. 44, 1249–1257.
• 5)   Macko,  S.A. and  Estep,  M.L.F. (1984) Microbial alteration of stable nitrogen and carbon isotopic compositions of organic matter. Org. Geochem. 6, 787–790.
• 6)   Macko,  S.A.,  Fogel,  M.L.,  Hare,  P.E. and  Hoering,  T.C. (1987) Isotopic fractionation of nitrogen and carbon in the synthesis of amino acids by microorganisms. Chem. Geol. 65, 79–92.
• 7)   Macko,  S.A.,  Fogel,  E.M.L.,  Engel,  M.H. and  Hare,  P.E. (1987) Kinetic fractionation of stable nitrogen isotopes during amino acid transamination. Geochim. Cosmochim. Acta 50, 2143–2146.
• 8)   Hare,  P.E.,  Fogel,  M.L.,  Stafford,  T.W. Jr.,  Mitchell,  A.D. and  Hoering,  T.C. (1991) The isotopic composition of carbon and nitrogen in individual amino acids isolated from modern and fossil proteins. J. Archaeol. Sci. 18, 277–292.
• 9)   Minagawa,  M.,  Egawa,  S.,  Kabaya,  Y. and  Karasawa-Tsuru,  K. (1992) Carbon and nitrogen isotope analysis for amino acids from biological sample. J. Mass Spectrom. Soc. Jpn. 40, 47–56.
• 10)   Yoneyama,  T.,  Kamachi,  K.,  Yamaya,  T. and  Mae,  T. (1993) Fractionation of nitrogen isotopes by glutamine synthase isolated from spinach leaves. Plant Cell Physiol. 34, 489–491.
• 11)  Koch, P.L., Fogel, M.L. and Tuross, N. (1994) Tracing the diets of fossil animals using stable isotopes. In Stable Isotopes in Ecology and Environmental Science (eds. Lajtha, K. and Michener, B). Blackwell Scientific Publication, Boston, pp. 63–92.
• 12)   Merritt,  D.A. and  Hayes,  J.M. (1994) Nitrogen isotopic analyses by isotope-ratio-monitoring gas chromatography/mass spectrometry. J. Am. Soc. Mass Spectrom. 5, 387–397.
• 13)   Metges,  C.C.,  Petzke,  K.-J. and  Henning,  U. (1996) Gas chromatography/combustion/isotope ratio mass spectrometric composition of N-acetyl- and N-pivaloyl amino acid esters to measure ¹⁵N isotopic abundances in physiological samples: a pilot study on amino acid synthesis in the upper gastro-intestinal tract of minipigs. J. Mass Spectrom. 31, 367–376.
• 14)   Owens,  N.J.P. (1987) Natural variations in ¹⁵N in the marine environment. Adv. Mar. Biol. 24, 389–451.
• 15)   Miyake,  Y. and  Wada,  E. (1967) The abundance ratio of ¹⁵N/¹⁴N in marine environments. Rec. Oceanogr. Works Jpn. 9, 37–53.
• 16)   DeNiro,  M.J. and  Epstein,  S. (1981) Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–351.
• 17)   Minagawa,  M. and  Wada,  E. (1984) Stepwise enrichment of ¹⁵N along food chains: further evidence and the relations between δ¹⁵N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140.
• 18)   Peterson,  B.J. and  Fry,  B. (1987) Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320.
• 19)   Post,  D.M. (2002) Using stable isotopes to estimated trophic position: Models, methods, and assumptions. Ecology 83, 703–718.
• 20)   Ambrose,  S.H. and  DeNiro,  M.J. (1986) Reconstruction of African human diet using bone collage carbon and nitrogen isotope ratios. Nature 319, 321–324.
• 21)   Wada,  E.,  Terazaki,  M.,  Kabaya,  Y. and  Nemoto,  T. (1987) ¹⁵N and ¹³C abundances in the Antarctic Ocean with emphasis on the biogeochemical structure of the food web. Deep-Sea Res. 34, 829–841.
• 22)   Wada,  E. and  Yoshioka,  T. (1995) Isotope biogeochemistry of several aquatic ecosystems. Geochem. Int. 32, 121–140.
• 23)  Fry, B. (2006) Stable Isotope Ecology. Springer, New York.
• 24)   Hobson,  K.A. and  Wassenaar,  L.I. (1999) Stable isotope ecology: an introduction. Oecologia 120, 312–313.
• 25)   Schoeninger,  M.J. (1984) Nitrogen and carbon isotopic composition of bone collagen from marine and terrestrial animals. Geochim. Cosmochim. Acta 48, 625–639.
• 26)   DeNiro,  M.J. and  Epstein,  S. (1981) Influence of diet in the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–351.
• 27)   McClelland,  J.W. and  Montoya,  J.P. (2002) Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180.
• 28)   Chikaraishi,  Y.,  Kashiyama,  Y.,  Ogawa,  N.O.,  Kitazato,  H. and  Ohkouchi,  N. (2007) Metabolic control of nitrogen isotope composition of amino acids in macroalgae and gastropods: implications for aquatic food web studies. Mar. Ecol. Prog. Ser. 342, 85–90.
• 29)   Chikaraishi,  Y.,  Ogawa,  N.O.,  Kashiyama,  Y.,  Takano,  Y.,  Suga,  H.,  Tomitani,  A. et al. (2009) Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750.
• 30)  Chikaraishi, Y., Ogawa, N.O. and Ohkouchi, N. (2010) Further evaluation of the trophic level estimation based on nitrogen isotopic composition of amino acids. In Earth, Life, and Isotopes (eds. Ohkouchi, N., Tayasu, I. and Koba, K.). Kyoto University Press, Kyoto, pp. 37–51.
• 31)   McCarthy,  M.D.,  Benner,  R.,  Lee,  C. and  Fogel,  M.L. (2007) Amino acid nitrogen isotopic fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic matter. Geochim. Cosmochim. Acta 71, 4727–4744.
• 32)  Popp, B.N., Graham, B.S., Olson, R.J., Hannides, C.C.S., Lott, M., Lopez-Ibarra, G. et al. (2007) Insight into the trophic ecology of yellowfin tuna, Thunnus albacares, from compound-specific nitrogen isotope analysis of proteinaceous amino acids. In Stable Isotopes as Indicators of Ecological Change (eds. Dawson, T.E. and Siegwolf, R.). Academic Press, Amsterdam, pp. 173–190.
• 33)   Styring,  A.K.,  Sealy,  J.C. and  Evershed,  R.P. (2010) Resolving the bulk δ¹⁵N values of ancient collagen via compound-specific nitrogen isotope analysis of constituent amino acids. Geochim. Cosmochim. Acta 74, 241–251.
• 34)   Ohkouchi,  N.,  Chikaraishi,  Y.,  Close,  H.G.,  Fry,  B.,  Larsen,  T.,  Madigan,  D.J. et al. (2017) Progresses and challenges with the use of amino acid nitrogen isotopic compositions in ecological and biogeochemical studies. Org. Geochem. 113, 150–174.
• 35)   Rishavy,  M.A.,  Cleland,  W.W. and  Lusty,  C.L. (2000) ¹⁵N Isotope effects in glutamine hydrolysis catalyzed by carbamyl phosphate synthase: Evidence for a tetrahedral intermediate in the mechanism. Biochemistry 39, 7309–7315.
• 36)   Sacks,  G.L. and  Brenna,  J.T. (2005) ¹⁵N/¹⁴N position-specific isotopic analyses of polynitrogenous amino acids. Anal. Chem. 77, 1013–1019.
• 37)   Zhang,  L. and  Altabet,  M.A. (2008) Amino-group-specific natural abundance nitrogen isotope ratio analysis in amino acids. Rapid Commun. Mass Spectrom. 22, 559–566.
• 38)   Carsten,  D.,  Lehmann,  M.F.,  Hofstetter,  T.B. and  Schubert,  C.J. (2013) Amino acid nitrogen isotopic composition patterns in lacustrine sedimenting matter. Geochim. Cosmochim. Acta 121, 328–338.
• 39)   McMahon,  K.W. and  McCarthy,  M.D. (2016) Embracing variability in amino acid δ¹⁵N fractionation: mechanisms, implications, and applications for trophic ecology. Ecosphere 7, e01511.
• 40)   Wu,  G. (2009) Amino acids metabolism, functions, and nutrition. Amino Acids 37, 1–17.
• 41)  Bender, D.A. (2012) Amino Acid Metabolism, 3rd Edition. Wiley-Blackwell, Chichester.
• 42)   Rishavy,  M.A. and  Cleland,  W.W. (2000) C and N kinetic isotope effects on the reaction of aspartate aminotransferase and the tyrosine-225 to phenylalanine mutant. Biochemistry 39, 7546–7551.
• 43)   Goto,  A.S.,  Miura,  K.,  Korenaga,  T.,  Hasegawa,  T.,  Ohkouchi,  N. and  Chikaraishi,  Y. (2018) Fractionation of stable nitrogen isotopes (¹⁵N/¹⁴N) during enzymatic deamination of glutamic acid: Implication for mass and energy transfers in the biosphere. Geochem. J. 52, 273–280.
• 44)   Cleland,  W.W. (2008) Use of isotope effects to elucidate enzyme mechanisms. CRC Crit. Rev. Biochem. 13, 385–428.
• 45)   Bergström,  J.,  Fürst,  P.,  Norée,  L.L. and  Vinnars,  E. (1974) Intracellular free amino acid concentration in human muscle tissue. J. Appl. Physiol. 36, 693–697.
• 46)   Koch,  A.L. (1962) The evaluation of the rates of biological processes from tracer kinetic data. I. The influence of labile metabolic pools. J. Theor. Biol. 3, 283–303.
• 47)   Overman,  N.C. and  Parrish,  D.L. (2001) Stable isotope composition of walleye: ¹⁵N accumulation with age and area-specific defferences in δ¹³C. Can. J. Fish. Aquat. Sci. 58, 1253–1260.
• 48)   Wagenmakers,  A.J. (1999) Tracers to investigate protein and amino acid metabolism in human subjects. Proc. Nutr. Soc. 58, 987–1000.
• 49)  Munro, H.N. (1970) Free amino acid pools and their role in regulation. In Mammalian Protein Metabolism, Vol. IV (ed. Munro, H.N.). Elsevier, Amsterdam, pp. 299–386.
• 50)  Houlihan, D.F., Mathers, E. and Foster, A.R. (1993). Biochemical correlates of growth rate in fish. In Fish Ecophysiology (eds. Rankin, J.C. and Jensen, J.B.). Chapman and Hall, London, pp. 45–70.
• 51)   Steel,  K.W. and  Daniel,  R.M. (1978) Fractionation of nitrogen isotopes by animals: a further complication to the use of variations in the natural abundance of ¹⁵N for tracer studies. J. Agric. Sci. Camb. 90, 7–9.
• 52)   Salvarina,  I.,  Yohannes,  E.,  Siemers,  B.M. and  Koselj,  K. (2013) Advantage of using fecal samples for stable isotope analysis in bats: evidence from a triple isotopic experiment. Rapid Commun. Mass Spectrom. 27, 1945–1953.
• 53)   Sutoh,  M.,  Koyama,  T. and  Yoneyama,  K. (1987) Variations of natural ¹⁵N abundances in the tissues and digesta of domestic animals. Radioisotopes 36, 74–77.
• 54)   Kuhnle,  G.G.C.,  Joosen,  A.M.C.P.,  Kneale,  C.J. and  O’Connell,  T.C. (2013) Carbon and nitrogen isotopic ratios of urine and faces as novel nutritional biomarkers of meat and fish intake. Eur. J. Nutr. 52, 389–395.
• 55)   Ohkouchi,  N.,  Ogawa,  N.O.,  Chikaraishi,  Y.,  Tanaka,  H. and  Wada,  E. (2015) Biochemical and physiological bases for the application of carbon and nitrogen isotopes to the environmental and ecological studies. Prog. Earth Planet. Sci. 2, 1–17.
• 56)   Hobson,  K.A.,  Alisaukas,  R.T. and  Clark,  R.G. (1993) Stable-nitrogen isotope enrichment in avian tissues due to fasting and nutritional stress: implications for isotopic analysis of diet. Condor 95, 388–394.
• 57)   Newsome,  S.D.,  Clementz,  M.T. and  Koch,  P.L. (2010) Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572.
• 58)  Wada, E., Ando, T. and Kumazawa, K. (1995) Biodiversity of stable isotope ratios. In Stable Isotopes in the Biosphere (eds. Wada, E., Yoneyama, T., Minagawa, M., Ando, T. and Fry, B.D.). Kyoto University Press, Kyoto, pp. 7–14.
• 59)   Wada,  E. (2009) Stable δ¹⁵N and δ¹³C isotope ratios in aquatic ecosystems. Proc. Jpn. Acad. Ser. B 85, 98–107.
• 60)   McCarthy,  M.D.,  Lehman,  J. and  Kudela,  R. (2013) Compound-specific amino acid δ¹⁵N patterns in marine algae: Tracer potential for cyanobacterial vs. eukaryotic organic nitrogen sources in the ocean. Geochim. Cosmochim. Acta 103, 104–120.
• 61)   Bradley,  C.J.,  Wallsgrove,  N.J.,  Choy,  C.A.,  Drazen,  J.C.,  Hetherington,  E.D.,  Hoen,  D.K. et al. (2015) Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. Limnol. Oceanogr. Methods 13, 476–493.
• 62)   Chikaraishi,  Y.,  Ogawa,  N.O.,  Doi,  H. and  Ohkouchi,  N. (2011) ¹⁵N/¹⁴N ratios of amino acids as a tool for studying terrestrial food webs: a case study of terrestrial insects (bees, wasps, and hornets). Ecol. Res. 26, 835–844.
• 63)   Ishikawa,  N.F.,  Kato,  Y.,  Togashi,  H.,  Yoshimura,  M.,  Yoshimizu,  C.,  Okuda,  N. et al. (2014) Stable nitrogen isotopic composition of amino acids reveals food web structure in stream ecosystems. Oecologia 175, 911–922.
• 64)   Takizawa,  Y.,  Dharampal,  P.S.,  Steffan,  S.A.,  Takano,  Y.,  Ohkouchi,  N. and  Chikaraishi,  Y. (2017) Intra-trophic isotopic discrimination of ¹⁵N/¹⁴N for amino acids in autotrophs: implications for nitrogen dynamics in ecological studies. Ecol. Evol. 7, 2916–2924.
• 65)   O’Connell,  T.C. (2017) ‘Trophic’ and ‘source’ amino acids in trophic estimation: a likely metabolic explanation. Oecologia 184, 317–326.
• 66)   Junk,  G. and  Svec,  H. (1958) The absolute abundance of nitrogen isotopes in the atmosphere and compressed gas from various sources. Geochim. Cosmochim. Acta 14, 234–243.
• 67)   Steffan,  S.A.,  Chikaraishi,  Y.,  Horton,  D.R.,  Ohkouchi,  N.,  Singleton,  M.E.,  Miliczky,  E. et al. (2013) Trophic hierarchies illuminated via amino acid isotopic analysis. PLoS One 8, e76152.
• 68)   Bowes,  R.E. and  Thorp,  J.H. (2015) Consequences of employing amino acid vs. bulk-tissue, stable isotope analysis: a laboratory trophic position experiment. Ecosphere 6, article 14.
• 69)   Ishikawa,  N.F.,  Hayashi,  F.,  Sasaki,  Y.,  Chikaraishi,  Y. and  Ohkouchi,  N. (2017) Trophic discrimination factor of nitrogen isotopes within amino acids in the dobsonfly Protohermes grandis (Megaloptera: Corydalidae) larvae in a controlled feeding experiment. Ecol. Evol. 7, 1674–1679.
• 70)   Germain,  L.R.,  Koch,  P.L.,  Harvey,  J. and  McCarthy,  M.D. (2013) Nitrogen isotope fractionation in amino acids from harbor seals: implications for compound-specific trophic position calculations. Mar. Ecol. Prog. Ser. 482, 265–277.
• 71)   Hoen,  D.K.,  Kim,  S.L.,  Hussey,  N.E.,  Wallsgrove,  N.J.,  Drazen,  J.C. and  Popp,  B.N. (2014) Amino acid ¹⁵N trophic enrichment factors of four large carnivorous fishes. J. Exp. Mar. Biol. Ecol. 453, 76–83.
• 72)   Nuche-Pascual,  M.T.,  Riuz-Cooley,  R.I. and  Herzka,  S.Z. (2021) A meta-analysis of amino acid δ¹⁵N trophic enrichment factors in fishes relative to nutritional and ecological drivers. Ecosphere 12, e03570.
• 73)   Chikaraishi,  Y.,  Steffan,  S.A.,  Takano,  Y. and  Ohkouchi,  N. (2015) Diet quality influences isotopic discrimination among amino acids in an aquatic vertebrate. Ecol. Evol. 5, 2048–2059.
• 74)   McMahon,  K.W.,  Thorrold,  S.R.,  Elsdon,  T.S. and  McCarthy,  M.D. (2015) Trophic discrimination of nitrogen stable isotopes in amino acids varies with diet quality in a marine fish. Limnol. Oceanogr. 60, 1076–1087.
• 75)   Bradley,  C.J.,  Madigan,  D.J.,  Block,  B.A. and  Popp,  B.N. (2015) Amino acid isotope incorporation and enrichment factors in Pacific bluefin tuna, Thunnus orientalis. PLoS One 9, e85818.
• 76)   Decima,  M.,  Landry,  M.R. and  Popp,  B.N. (2013) Environmental perturbation effects on baseline δ¹⁵N values and zooplankton trophic flexibility in the southern California Current Ecosystem. Limnol. Oceanogr. 58, 624–634.
• 77)  Ohkouchi, N. and Takano, Y. (2014) Organic nitrogen: Sources, fates, and chemistry. In Treatise on Geochemistry, 2nd edition (eds. Falkowski, P.G. and Freeman, K.H.). Elsevier, Amsterdam, pp. 251–289.
• 78)   Vander Zanden,  H.B.,  Arthur,  K.E.,  Bolten,  A.B.,  Popp,  B.N.,  Lagueux,  C.J.,  Harrison,  E. et al. (2013) Trophic ecology of a green turtle breeding population. Mar. Ecol. Prog. Ser. 476, 237–249.
• 79)   Choy,  C.A.,  Davison,  P.C.,  Drazen,  J.C.,  Flynn,  A.,  Gier,  E.J.,  Hoffman,  J.C. et al. (2012) Global trophic position comparison of two dominant mesopelagic fish families (Myctophidae, Stomiidae) using amino acid nitrogen isotopic analyses. PLoS One 7, e50133.
• 80)   Engel,  M.H. and  Macko,  S.A. (1986) Stable isotope evaluation of the origins of amino acids in fossils. Nature 323, 531–533.
• 81)   Macko,  M.A.,  Uhle,  M.E.,  Engel,  M.H. and  Andrusevich,  V. (1997) Stable nitrogen isotope analysis of amino acid enantiomers by gas chromatography/combustion/isotope ratio mass spectrometry. Anal. Chem. 69, 926–929.
• 82)   Veuger,  B.,  Middelburg,  J.J.,  Boschker,  T.S. and  Houtekamer,  M. (2005) Analysis of ¹⁵N incorporation into D-alanine: A new method for tracing nitrogen uptake by bacteria. Limnol. Oceanogr. Methods 3, 230–240.
• 83)   Yamaguchi,  Y. and  McCarthy,  M.D. (2018) Sources and transformations of dissolved and particulate organic nitrogen in the North Pacific Subtrophical Gyre indicated by compound-specific δ¹⁵N analysis of amino acids. Geochim. Cosmochim. Acta 220, 329–347.
• 84)   Serban,  A.,  Engel,  M.H. and  Macko,  S.A. (1987) The distribution, stereochemistry and stable isotopic composition of amino acid constituents of fossil and modern mollusk shells. Org. Geochem. 13, 1123–1129.
• 85)   Takano,  Y.,  Chikaraishi,  Y.,  Ogawa,  N.O.,  Kitazato,  H. and  Ohkouchi,  N. (2009) Compound-specific analysis of D-, L-alanine and valine: application of diastereomer separation to δ¹⁵N and microbial peptidoglycan studies. Anal. Chem. 81, 394–399.
• 86)  Takano, Y., Chikaraishi, Y. and Ohkouchi, N. (2010) Enantiomer-specific isotope analysis of D- and L-alanine: Nitrogen isotopic hetero- and homogeneity in microbial and chemical processes. In Earth, Life and Isotopes (eds. Ohkouchi, N., Tayasu, I. and Koba, K.). Kyoto University Press, Kyoto, pp. 387–402.
• 87)   Mizutani,  H.,  Kabaya,  Y. and  Wada,  E. (1991) Nitrogen and carbon isotope compositions relate linearlity in cormorant tissues and its diet. Isotopenpraxis 27, 166–168.
• 88)   Kudo,  Y. and  Wada,  E. (2020) Ratio of naturally retained ¹⁵N to ¹³C in rat brain regions as a marker of brain function and activity. Neurosci. Res. 160, 32–42.
• 89)   Schmidt,  K.,  McClelland,  J.W.,  Mente,  E.,  Montoya,  J.P.,  Atkinson,  A. and  Voss,  M. (2004) Trophic-level interpretation based on δ¹⁵N values: implications of tissue-specific fractionation and amino acid composition. Mar. Ecol. Prog. Ser. 266, 43–58.
• 90)   Kruger,  B.R.,  Werne,  J.P.,  Branstrator,  D.K.,  Hrabik,  T.R.,  Chikaraishi,  Y.,  Ohkouchi,  N. et al. (2016) Organic matter transfer in Lake Superior’s food web: Insights from bulk and molecular stable isotope and radiocarbon analyses. Limnol. Oceanogr. 61, 149–164.
• 91)   Branstrator,  D.K.,  Cabana,  G.,  Mazumder,  A. and  Rasmussen,  J.B. (2000) Measuring life-history omnivory in the opossum shrimp, Mysis relicta, with stable nitrogen isotopes. Limnol. Oceanogr. 45, 463–467.
• 92)   Cabana,  G. and  Rasmussen,  J.B. (1994) Modelling food chain structure and contaminant bioaccumulation using stable nitrogen isotopes. Nature 372, 255–257.
• 93)   Gamble,  A.E.,  Hrabil,  T.R.,  Stockwell,  J.D. and  Yule,  D.L. (2011) Trophic connections in Lake Superior Part I: The offshore fish community. J. Great Lakes Res. 37, 541–549.
• 94)   Ahrenstorff,  T.D.,  Hrabil,  T.R.,  Jacobson,  P.C. and  Pereira,  D.L. (2013) Food resource effects on diel movements and body size of cisco in north-temperate lakes. Oecologia 173, 1309–1320.
• 95)   Ohkouchi,  N.,  Shibata,  H.,  Nomaki,  H.,  Ogawa,  N.O.,  Chikaraishi,  Y.,  Goto,  T. et al. (2016) A monitoring result of polychlorinated biphenyls (PCBs) in deep-sea organisms and sediments off Tohoku during 2012–2014: temporal variation and the relationship with the trophic position. J. Oceanogr. 72, 629–639.
• 96)   Sherwood,  O.A.,  Lehmann,  M.F.,  Schubert,  C.J.,  Scott,  D.B. and  McCarthy,  M.D. (2011) Nutrient regime shift in the western North Atlantic indicated by compound-specific δ¹⁵N of deep-sea gorgonian corals. Proc. Natl. Acad. Sci. U.S.A. 108, 1011–1015.
• 97)   Vokhshoori,  N.L. and  McCarthy,  M.D. (2014) Compound-specific δ¹⁵N amino acid measurements in littoral mussels in the California upwelling ecosystem: A new approach to generating baseline δ¹⁵N isoscapes for coastal ecosystems. PLoS One 9, e98087.
• 98)  Kozhov, M. (1963) Lake Baikal and Its Life. Dr. W. Junk Publishers, The Hague.
• 99)   Yoshii,  K.,  Melnik,  N.G.,  Timoshkin,  O.A.,  Bondarenko,  N.A.,  Anoshko,  P.N.,  Yoshioka,  T. et al. (1999) Stable isotope analysis of the pelagic food web in Lake Baikal. Limnol. Oceanogr. 44, 502–511.
• 100)   Vander Zanden,  M.J. and  Fetzer,  W.W. (2007) Global patterns of aquatic food chain length. Oikos 116, 1378–1388.
• 101)   Post,  D.M.,  Pace,  M.L. and  Halrston,  N.G. Jr. (2000) Ecosystem size determines food-chain length in lakes. Nature 405, 1047–1049.
• 102)   Hyodo,  F.,  Nishikawa,  J.,  Kohzu,  A.,  Fujita,  N.,  Saizen,  I.,  Tsogtbaatar,  J. et al. (2012) Variation in nitrogen isotopic composition in the Selenga River watershed, Mongolia. Limnology 13, 155–161.
• 103)   Kato,  Y.,  Togashi,  H.,  Kurita,  Y.,  Osada,  Y.,  Amano,  Y.,  Yoshimizu,  C. et al. (2022) Segmental isotope analysis of the vertebral centrum reveals the spatiotemporal population structure of adult Japanese flounder Paralichthys olivaceus in Sendai Bay, Japan. Mar. Biol. 168, 57.
• 104)   Eltoum,  I.,  Fredenburgh,  J.,  Myers,  R.B. and  Grizzle,  W.E. (2001) Introduction to the theory and practice of fixation of tissues. J. Histotechnol. 24, 173–190.
• 105)   Ogawa,  N.O.,  Chikaraishi,  Y. and  Ohkouchi,  N. (2013) Trophic position estimates of formalin-fixed samples with nitrogen isotopic compositions of amino acids: an application to gobiid fish (Isaza) in Lake Biwa, Japan. Ecol. Res. 28, 697–702.
• 106)   Swalethorp,  R.,  Aluwihare,  L.,  Thompson,  A.R.,  Ohman,  M.D. and  Landry,  M.R. (2020) Errors associated with compound-specific δ¹⁵N analysis of amino acids in preserved fish samples purified by high-pressure liquid chromatography. Limnol. Oceanogr. Methods 18, 259–270.
• 107)   Ogawa,  N.O.,  Koitabashi,  T.,  Oda,  H.,  Nakamura,  T.,  Ohkouchi,  N. and  Wada,  E. (2001) Fluctuations of nitrogen isotope ratio of gobiid fish specimens and sediments in Lake Biwa during the 20th century. Limnol. Oceanogr. 46, 1228–1236.
• 108)   Nakazawa,  T.,  Skai,  Y.,  Hsieh,  C.-H.,  Koitabashi,  T.,  Tayasu,  I.,  Yamamura,  N. et al. (2010) Is the relationship between body size and trophic niche position time-invariant in a predatory fish? First stable isotope evidence. PLoS One 5, e9120.
• 109)   Nakano,  T.,  Tayasu,  I.,  Wada,  E.,  Igeta,  A.,  Hyodo,  F. and  Miura,  Y. (2005) Sulfur and strontium isotope geochemistry of tributary rivers of Lake Biwa; implications for human impact on the decadal change of lake water quality. Sci. Total Environ. 345, 1–12.
• 110)   Nishikawa,  J.,  Kohzu,  A.,  Boontanon,  N.,  Iwata,  T.,  Tanaka,  T.,  Ogawa,  N.O. et al. (2009) Isotopic composition of nitrogenous compounds with emphasis on anthropogenic loading in river ecosystems. Isotopes Environ. Health Stud. 45, 27–40.
• 111)   Ishikawa,  N.F.,  Chikaraishi,  Y.,  Takano,  Y.,  Sasaki,  Y.,  Takizawa,  Y.,  Tsuchiya,  M. et al. (2018) A new analytical method for the determination of nitrogen isotopic composition of methionine: its application in aquatic ecosystems with mixed resources. Limnol. Oceanogr. Methods 16, 607–620.
• 112)   Herbert,  C.E.,  Popp,  B.N.,  Fernie,  K.J.,  Ka’apu-Lyons,  C.,  Rattner,  B.A. and  Wallsgrove,  N. (2016) Amino acid specific stable nitrogen isotope values in avian tissues: Insights from captive American kestrels and wind herring gulls. Environ. Sci. Technol. 50, 12928–12937.
• 113)   Choi,  B.,  Ha,  S.-Y.,  Lee,  J.-S.,  Chikaraishi,  Y.,  Ohkouchi,  N. and  Shin,  K.-H. (2017) Trophic interaction among organisms in a seagrass meadow ecosystem as revealed by bulk δ¹³C and amino acid δ¹⁵N analyses. Limnol. Oceanogr. 62, 1426–1435.
• 114)   Wada,  E. and  Yoshioka,  T. (1995) Isotope biogeochemistry of several aquatic ecosystems. Geochem. Int. 32, 121–140.
• 115)   Gannes,  L.Z.,  O’Brien,  D.M. and  Martinez del Rio,  C. (1997) Stable isotopes in animal ecology: Assumptions, caveats, and a call for more laboratory experiments. Ecology 78, 1271–1276.
• 116)  Fenchel, T. and Finlay, B.J. (1995) Ecology and Evolution in Anoxic Worlds. Oxford University Press, Oxford.
• 117)  Kirchman, D.L. (2000) Microbial Ecology of the Oceans. Wiley-Liss, New York.
• 118)   Yamaguchi,  Y.,  Chikaraishi,  Y.,  Takano,  Y.,  Ogawa,  N.O.,  Imachi,  H.,  Yokoyama,  Y. et al. (2017) Fractionation of nitrogen isotopes during amino acid metabolism in heterotrophic and chemolithoautotrophic microbes across Eukarya, Bacteria, and Archaea: Effects of nitrogen sources and metabolic Pathways. Org. Geochem. 111, 101–112.
• 119)   Maki,  K.,  Ohkouchi,  N.,  Chikaraishi,  Y.,  Fukuda,  H.,  Miyajima,  T. and  Nagata,  T. (2014) Influences of nitrogen substrates and substrate C:N ratios on the nitrogen isotopic composition of amino acids from the marine bacterium Vibrio harveyi. Geochim. Cosmochim. Acta 140, 521–530.
• 120)   Steffan,  S.A.,  Chikaraishi,  Y.,  Currie,  C.,  Gaines Day,  H.R.,  Pauli,  J.N.,  Zalapa,  J.E. et al. (2015) Microbes are trophic analogs of animals: Unification of the micro- and macrobiome in food web ecology. Proc. Natl. Acad. Sci. U.S.A. 112, 15119–15124.
• 121)   Uhle,  M.E.,  Macko,  S.A.,  Spero,  J.J.,  Engel,  M.H. and  Lea,  D.W. (1997) Sources of carbon and nitrogen in modern planktonic foraminifera: the role of algal symbionts as determined by bulk and compound specific stable isotopic analyses. Org. Geochem. 27, 103–113.
• 122)   Maeda,  T.,  Hirose,  E.,  Chikaraishi,  Y.,  Kawato,  M.,  Takishita,  K.,  Yoshida,  T. et al. (2012) Algivore or phototroph? Plakobranchus ocellatus (Gastropoda) continuously acquires kleptoplasts and nutrition from multiple algal species in nature. PLoS One 7, e42024.
• 123)   Nomaki,  H.,  Chikaraishi,  Y.,  Tsuchiya,  M.,  Toyofuku,  T.,  Ohkouchi,  N. and  Kitazato,  H. (2014) Cellular amino acids nitrogen isotopic composition as an indicator of denitrification in benthic foraminifera under anoxic conditions. Limnol. Oceanogr. 59, 1879–1888.
• 124)   Tsuchiya,  M.,  Chikaraishi,  Y.,  Nomaki,  H.,  Sasaki,  Y.,  Tame,  A.,  Uematsu,  K. et al. (2018) Compound-specific isotope analysis of benthic foraminifer amino acids suggests microhabitat variability in rocky-shore environments. Ecol. Evol. 8, 8380–8395.
• 125)  West, J.B., Bowen, G.J., Dawson, T.E. and Tu, K.P. (2010) Isoscapes: Understanding Movement, Pattern, and Process on Earth through Isotope Mapping. Springer, Dordrecht.
• 126)   Bowen,  G.J. (2010) Isoscapes: Spatial pattern in isotopic biogeochemistry. Annu. Rev. Earth Planet. Sci. 38, 161–187.
• 127)   McMahon,  K.W.,  Hamady,  L.L.,  Simon,  R. and  Thorrold,  S.R. (2013) A review of ecogeochemistry approaches to estimating movements of marine animals. Limnol. Oceanogr. 58, 697–714.
• 128)  McMahon, K.W. and Newsome, S.D. (2019) Amino acid isotope analysis: A new frontier in studies of animal migration and foraging ecology. In Tracking Animal Migration with Stable Isotopes (eds. Hobson, K.A. and Wassenaar, L.I.). Academic Press, London, pp. 173–190.
• 129)   Lorrain,  A.,  Graham,  B.,  Popp,  B.N.,  Allain,  V.,  Olson,  R.J.,  Hunt,  B.P.V. et al. (2015) Nitrogen isotopic baselines and implications for estimating foraging habitat and trophic position of yellowfin tuna in the Indian and Pacific Oceans. Deep-Sea Res. 113, 188–198.
• 130)   Matsubayashi,  J.,  Osada,  Y.,  Tadokoro,  K.,  Abe,  Y.,  Yamaguchi,  A.,  Shirai,  K. et al. (2020) Tracking long-distance ocean migration of marine fishes using compound-specific stable isotope analysis of amino acids. Ecol. Lett. 23, 881–890.
• 131)   Matsubayashi,  J.,  Saitoh,  Y.,  Osada,  Y.,  Uehara,  Y.,  Habu,  J.,  Sasaki,  T. et al. (2017) Incremental analysis of vertebral centra can reconstruct the stable isotope chronology of teleost fishes. Methods Ecol. Evol. 8, 1755–1763.
• 132)   Matsubayashi,  J.,  Umezawa,  Y.,  Matsuyama,  M.,  Kawabe,  R.,  Mei,  W.,  Wan,  X. et al. (2019) Using segmental isotope analysis of teleost fish vertebrae to estimate trophic discrimination factors of bone collagen. Limnol. Oceanogr. Methods 17, 87–96.
• 133)   Urawa,  S.,  Ueno,  Y.,  Ishida,  Y.,  Seeb,  L.W.,  Crane,  P.A.,  Abe,  S. et al. (2001) A migration model of Japanese chum salmon during early ocean life. N. Pac. Anadr. Fish. Comm. Tech. Rep. 2, 45–46.
• 134)   Seeb,  L.W.,  Crane,  P.A.,  Kondzela,  C.M.,  Wilmot,  R.L.,  Urawa,  S.,  Varnavskaya,  N.V. et al. (2004) Migration of Pacific Rim chum salmon on the high seas: insights from genetic data. Environ. Biol. Fishes 69, 21–36.
• 135)   Wallace,  A.A.,  Hollander,  D.J. and  Peebles,  E.B. (2014) Stable isotopes in fish eye lenses as potential recorders of trophic and geographic history. PLoS One 9, e108935.
• 136)   Bell-Tilcock,  M.,  Jeffres,  C.A.,  Rypel,  A.L.,  Sommer,  T.R.,  Katz,  J.V.E.,  Whitman,  G. et al. (2021) Advancing diet reconstruction in fish eye lenses. Methods Ecol. Evol. 12, 449–457.
• 137)   Harada,  Y.,  Ito,  S.-I.,  Ogawa,  N.O.,  Yoshikawa,  C.,  Ishikawa,  N.F.,  Yoneda,  M. et al. (2022) Compound-specific nitrogen isotope analysis in eye lenses as a new tool to reconstruct the geographic and trophic histories of fish. Front. Mater. Sci. 8, 796532.
• 138)   Miller,  M.J.,  Chikaraishi,  Y.,  Ogawa,  N.O.,  Yamada,  Y.,  Tsukamoto,  K. and  Ohkouchi,  N. (2012) A low trophic position of Japanese eel larvae indicates on feeding on marine snow. Biol. Lett. 9, 20120826.
• 139)   Fogel,  M.L. (1997) Biogeochemical record of ancient humans. Org. Geochem. 27, 275–287.
• 140)   Styring,  A.K.,  Fraser,  R.A.,  Arbogast,  R.-M.,  Halstead,  P.,  Isaakidou,  V.,  Pearson,  J.A. et al. (2015) Refining human palaeodietary reconstruction using amino acid δ¹⁵N values of plants, animals and humans. J. Archaeol. Sci. 53, 504–515.
• 141)  Evershed, R.P., Bull, I.D., Corr, L.T., Crossman, Z.M., van Dongen, B.E., Evans, C.J. et al. (2007) Compound-specific stable isotope analysis in ecology and paleoecology. In Stable Isotopes in Ecology and Environmental Science, 2nd edition (eds. Michner, R. and Lajtha, K.). Blackwell, Malden, pp. 480–540.
• 142)   Naito,  Y.,  Honch,  N.V.,  Chikaraishi,  Y.,  Ohkouchi,  N. and  Yoneda,  M. (2010) Quantitative evaluation of marine protein contribution in ancient diets based on nitrogen isotope ratios of individual amino acids in bone collagen: An investigation at the Kitakogane Jomon site. Am. J. Phys. Anthropol. 143, 31–40.
• 143)   Naito,  Y.I.,  Chikaraishi,  Y.,  Ohkouchi,  N.,  Mukai,  H.,  Shibata,  Y.,  Honch,  N.V. et al. (2010) Dietary reconstruction of the Okhotsk Culture of Hokkaido, Japan, based on nitrogen composition of amino acids: Implications for correction of ¹⁴C marine reservoir effects on human bones. Radiocarbon 52, 671–681.
• 144)   Naito,  Y.,  Chikaraishi,  Y.,  Ohkouchi,  N.,  Drucker,  D.G. and  Boscherens,  H. (2013) Nitrogen isotopic composition of collagen amino acids as a measure of human trophic position and aquatic resource consumption: insights from Mesolithic and Epipalaeolithic archaeological sites in France. World Archaeol. 45, 338–359.
• 145)   Naito,  Y.I.,  Chikaraishi,  Y.,  Drucker,  D.G.,  Ohkouchi,  N.,  Semal,  P.,  Wißing,  C. et al. (2016) Ecological niche of Neanderthals from Spy Cave revealed by nitrogen isotopes of individual amino acids in collagen. J. Hum. Evol. 93, 82–90.
• 146)   Itahashi,  Y.,  Chikaraishi,  Y.,  Ohkouchi,  N. and  Yoneda,  M. (2014) Refining the paleo food web reconstruction with the nitrogen isotopic composition of amino acids from bone collagen: A case study with archaeological herbivores from Tell el-Kerkh, Syria. Geochem. J. 48, e15–e19.
• 147)   Itahashi,  Y.,  Tsuneki,  A.,  Dougherty,  S.,  Chikaraishi,  Y.,  Ohkouchi,  N. and  Yoneda,  M. (2018) Dining together: Reconstruction of Neolithic food consumption based on the δ¹⁵N values for individual amino acids at Tell el-Kerkh, northern Levant. J. Archaeol. Sci. Rep. 17, 775–784.
• 148)   Itahashi,  Y.,  Selim Erdal,  Y.,  Tekin,  H.,  Miyake,  Y.,  Chikaraishi,  Y.,  Ohkouchi,  N. et al. (2019) Amino acid ¹⁵N analysis reveals change in the importance of freshwater resources between the hunter-gatherer and farmer in the Neolithic upper Tigris. Am. J. Phys. Anthropol. 168, 676–686.
• 149)   Rey,  L.,  Naito,  Y.I.,  Chikaraishi,  Y.,  Rottier,  S.,  Goude,  G. and  Ohkouchi,  N. (2022) Specifying subsistence strategies of early farmers: New results from compound-specific isotopic analysis of amino acids. Int. J. Osteoarchaeol. 32, 654–668.
• 150)   Yoneda,  M.,  Tanaka,  A.,  Shibata,  Y.,  Morita,  M.,  Uzawa,  K.,  Hirota,  M. et al. (2002) Radiocarbon marine reservoir effect in human remains from Kitakogane site, Hokkaido, Japan. J. Archaeol. Sci. 29, 529–536.
• 151)   Then,  A.Y.H.,  Adame,  M.F.,  Fry,  B.,  Chong,  V.C.,  Riekenberg,  P.M.,  Zakaria,  R.M. et al. (2021) Stable isotopes clearly track mangrove inputs and food web changes along a reforestation gradient. Ecosystem 24, 939–954.
• 152)   Conradt,  L. (2006) Use of a seaweed habitat by red deer (Cervus elaphus L.). J. Zool. 250, 541–549.
• 153)   DeNiro,  M.J. (1985) Postmortem preservation and alteration of in vivo bone collagen isotope ratios in relation to paleodietary reconstruction. Nature 317, 806–809.
• 154)   Naito,  Y.I.,  Chikaraishi,  Y.,  Ohkouchi,  N. and  Yoneda,  M. (2013) Evaluation of carnivory in inland Jomon hunter-gatherers based on nitrogen isotopic compositions of individual amino acids in bone collagen. J. Archaeol. Sci. 40, 2913–2923.
• 155)   Bada,  J.L.,  Schoeninger,  M.L. and  Schmmermann,  A. (1989) Isotopic fractionation during peptide bond hydrolysis. Geochim. Cosmochim. Acta 53, 3337–3341.
• 156)   Silfer,  J.A.,  Engel,  M.H. and  Macko,  S.A. (1992) Kinetic fractionation of stable carbon and nitrogen isotopes during peptide bond hydrolysis: Experimental evidence and geochemical implications. Chem. Geol. 101, 211–221.
• 157)   Bocherens,  H.,  Fizet,  M.,  Mariotti,  A.,  Lange-Badre,  B.,  Vandermeersch,  B.,  Borel,  J.P. et al. (1991) Isotopic biogeochemistry (¹³C, ¹⁵N) of fossil vertebrate collagen: application to the study of a past food web including Neanderthal man. J. Hum. Evol. 20, 481–492.
• 158)   Nakatomi,  N.,  Hirahara,  M.,  Natori,  N.,  Toda,  T. and  Yamamoto,  S. (2013) Change in metabolism and nitrogen isotopic composition of amino acids through egg production of the calanoid copepod Acartia steueri. Res. Org. Geochem. 29, 61–64.
• 159)   Båmstedt,  U. (1983) RNA concentration in zooplankton: seasonal variation in boreal species. Mar. Ecol. Prog. Ser. 11, 291–297.
• 160)   Jose Bullejos,  F.,  Carrillo,  P.,  Gorokhova,  E.,  Medina-Sanchez,  J.M. and  Villar-Argaiz,  M. (2014) Nucleic Acid Content in Crustacean Zooplankton: Bridging Metabolic and Stoichiometric Predictions. PLoS One 9, e86493.
• 161)   Strable,  M.S.,  Tschantz,  C.L.,  Varamini,  B.,  Chikaraishi,  Y.,  Ohkouchi,  N. and  Brenna,  J.T. (2011) Mammalian DNA δ¹⁵N exhibits 40‰ intramolecular variation and is unresponsive to dietary protein level. Rapid Commun. Mass Spectrom. 25, 3555–3562.
• 162)   Svensson,  E.,  Schouten,  S.,  Homans,  E.C.,  Middelburg,  J.J. and  Sinninghe Damste,  J.S. (2016) Factors controlling the stable nitrogen isotopic composition (δ¹⁵N) of lipids in marine animals. PLoS One 11, e0146321.
• 163)   Isaji,  Y.,  Ogawa,  N.O.,  Takano,  Y. and  Ohkouchi,  N. (2020) Quantification and carbon and nitrogen isotopic measurements of heme B in environmental samples. Anal. Chem. 92, 11213–11222.
• 164)   Pimm,  S.L.,  Lawton,  J.H. and  Cohen,  J.E. (1991) Food web patterns and their consequences. Nature 350, 669–674.
• 165)   Vander Zanden,  M.J. and  Rasmussen,  J.B. (2001) Variation in δ¹⁵N and δ¹³C trophic fractionation: implications for aquatic food web studies. Limnol. Oceanogr. 46, 2061–2066.
• 166)   Cowie,  G.L. and  Hedges,  J.I. (1992) Sources and reactivities of amino acids in a coastal marine environment. Limnol. Oceanogr. 37, 703–724.
• 167)   Wilson,  R.P. and  Cowey,  C.B. (1985) Amino acid composition of whole body tissue of rainbow trout and Atlantic salmon. Aquaculture 48, 373–376.
• 168)  Shiga Prefecture. (1951–1997) The regular observation in Lake Biwa. Fisheries Experimental Station of Shiga Prefecture, Japan (in Japanese).

Non-standard abbreviation list

FCL

food chain length

GDH

glutamate dehydrogenase

TDF

trophic discrimination factor

TP

trophic position

Profile

In 1966, Naohiko Ohkouchi was born in Kyoto City. He graduated from The University of Tokyo and received his Ph.D. from the Department of Geology, The University of Tokyo in 1990 and 1995, respectively. After working at prestigious institutes such as the Kyoto University, Hokkaido University and Woods Hole Oceanographic Institution, U.S.A., he joined the Institute for Frontier Research on Earth Evolution, Japan Agency for Marine-Earth Science and Technology (JAMSTEC) in 2002. Subsequently, he studied the radiocarbon dating of organic compounds isolated from sediments and was the first to demonstrate that climate change affects the dynamics of deep sea particles. Furthermore, he and his colleagues at JAMSTEC also worked on the use of nitrogen isotopic ratios of amino acids as a tool for ecosystem studies and chlorophylls and porphyrins for understanding biogeochemical cycles in the photic zone of natural environments. Moreover, Dr. Ohkouchi is currently Director General of Research Institute for Marine Resources Utilization and Director of Biogeochemistry Research Center, JAMSTEC. He was visiting professors at the Department of Environmental Chemistry and Engineering, Tokyo Institute of Technology (2011–2019) and at the Department of Earth and Planetary Science, The University of Tokyo (2012–2014). Dr. Ohkouchi was also the recipient of the Oceanochemistry Award in 2013 and the Duke of Edinburgh Prize of the Japan Academy in 2022.