Compound‐specific stable isotope analysis (
Ecologists increasingly determine the δ15N values of amino acids (AA) in animal tissue; “source” AA typically exhibit minor variation between diet and consumer, while “trophic” AA have increased δ15N values in consumers. Thus, trophic‐source δ15N offsets (i.e., Δ15NT‐S) reflect trophic position in a food web. However, even minor variations in δ15Nsource AAvalues may influence the magnitude of offset that represents a trophic step, known as the trophic discrimination factor (i.e., TDFT‐S). Diet digestibility and protein content can influence the δ15N values of bulk animal tissue, but the effects of these factors on AA Δ15NT‐Sand TDFT‐Sin mammals are unknown.
We fed captive mice (
As dietary protein increased, Δ15NConsumer‐Dietslightly declined for bulk muscle tissue in both experiments; increased for AA in the low‐fat, high‐fiber diet (A); and remained the same or decreased for AA in the high‐fat, low‐fiber diet (B). The effects of dietary protein on Δ15NT‐Sand on TDFT‐Svaried by AA but were consistent between variables.
Diets were less digestible and included more protein in Experiment A than in Experiment B. As a result, the mice in Experiment A probably oxidized more AA, resulting in greater Δ15NConsumer‐Dietvalues. However, the similar responses of Δ15NT‐Sand of TDFT‐Sto diet variation suggest that if diet samples are available, Δ15NT‐Saccurately tracks trophic position. If diet samples are not available, the patterns presented here provide a basis to interpret Δ15NT‐Svalues. The trophic‐source offset of Pro‐Lys did not vary across diets, and therefore may be more reliable for omnivores than other offsets (e.g., Glu‐Phe).
- NSF-PAR ID:
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Rapid Communications in Mass Spectrometry
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Compound‐specific stable isotope analysis (
CSIA) of amino acids ( AA) has rapidly become a powerful tool in studies of food web architecture, resource use, and biogeochemical cycling. However, applications to avian ecology have been limited because no controlled studies have examined the patterns in AAisotope fractionation in birds. We conducted a controlled CSIAfeeding experiment on an avian species, the gentoo penguin ( Pygoscelis papua), to examine patterns in individual AAcarbon and nitrogen stable isotope fractionation between diet (D) and consumer (C) (Δ13CC‐Dand Δ15NC‐D, respectively). We found that essential AA δ13C values and source AA δ15N values in feathers showed minimal trophic fractionation between diet and consumer, providing independent but complimentary archival proxies for primary producers and nitrogen sources respectively, at the base of food webs supporting penguins. Variations in nonessential AAΔ13CC‐Dvalues reflected differences in macromolecule sources used for biosynthesis (e.g., protein vs. lipids) and provided a metric to assess resource utilization. The avian‐specific nitrogen trophic discrimination factor ( TDFGlu‐Phe= 3.5 ± 0.4‰) that we calculated from the difference in trophic fractionation (Δ15 NC‐D) of glutamic acid and phenylalanine was significantly lower than the conventional literature value of 7.6‰. Trophic positions of five species of wild penguins calculated using a multi‐ TDFGlu‐Pheequation with the avian‐specific TDFGlu‐Phevalue from our experiment provided estimates that were more ecologically realistic than estimates using a single TDFGlu‐Pheof 7.6‰ from the previous literature. Our results provide a quantitative, mechanistic framework for the use of CSIAin nonlethal, archival feathers to study the movement and foraging ecology of avian consumers.
Compound‐specific stable isotope analysis of individual amino acids (CSIA‐AA) has emerged as a transformative approach to estimate consumer trophic positions (TPCSIA) that are internally indexed to primary producer nitrogen isotope baselines. Central to accurate TPCSIAestimation is an understanding of beta (
β) values—the differences between trophic and source AA δ15N values in the primary producers at the base of a consumers’ food web. Growing evidence suggests higher taxonomic and tissue‐specific βvalue variability than typically appreciated.
This meta‐analysis fulfils a pressing need to comprehensively evaluate relevant sources of
βvalue variability and its contribution to TPCSIAuncertainty. We first synthesized all published primary producer AA δ15N data to investigate ecologically relevant sources of variability (e.g. taxonomy, tissue type, habitat type, mode of photosynthesis). We then reviewed the biogeochemical mechanisms underpinning AA δ15N and βvalue variability. Lastly, we evaluated the sensitivity of TPCSIAestimates to uncertainty in mean βGlx‐Phevalues and Glx‐Phe trophic discrimination factors (TDFGlx‐Phe).
We show that variation in
βGlx‐Phevalues is two times greater than previously considered, with degree of vascularization, not habitat type (terrestrial vs. aquatic), providing the greatest source of variability (vascular autotroph = −6.6 ± 3.4‰; non‐vascular autotroph = +3.3 ± 1.8‰). Within vascular plants, tissue type secondarily contributed to βGlx‐Phevalue variability, but we found no clear distinction among C3, C4and CAM plant βGlx‐Phevalues. Notably, we found that vascular plant βGlx‐Lysvalues (+2.5 ± 1.6‰) are considerably less variable than βGlx‐Phevalues, making Lys a useful AA tracer of primary production sources in terrestrial systems. Our multi‐trophic level sensitivity analyses demonstrate that TPCSIAestimates are highly sensitive to changes in both βGlx‐Pheand TDFGlx‐Phevalues but that the relative influence of βvalues dissipates at higher trophic levels.
Our results highlight that primary producer
βvalues are integral to accurate trophic position estimation. We outline four key recommendations for identifying, constraining and accounting for βvalue variability to improve TPCSIAestimation accuracy and precision moving forward. We must ultimately expand libraries of primary producer AA δ15N values to better understand the mechanistic drivers of βvalue variation.
Nitrogen isotopic compositions (δ15N) of source and trophic amino acids (AAs) are crucial tracers of N sources and trophic enrichments in diverse fields, including archeology, astrobiochemistry, ecology, oceanography, and paleo‐sciences. The current analytical technique using gas chromatography‐combustion‐isotope ratio mass spectrometry (GC/C/IRMS) requires derivatization, which is not compatible with some key AAs. Another approach using high‐performance liquid chromatography‐elemental analyzer‐IRMS (HPLC/EA/IRMS) may experience coelution issues with other compounds in certain types of samples, and the highly sensitive nano‐EA/IRMS instrumentations are not widely available.
We present a method for high‐precision δ15N measurements of AAs (δ15N‐AA) optimized for canonical source AA‐phenylalanine (Phe) and trophic AA‐glutamic acid (Glu). This offline approach entails purification and separation via high‐pressure ion‐exchange chromatography (IC) with automated fraction collection, the sequential chemical conversion of AA to nitrite and then to nitrous oxide (N2O), and the final determination of δ15N of the produced N2O via purge‐and‐trap continuous‐flow isotope ratio mass spectrometry (PT/CF/IRMS).
The cross‐plots of δ15N of Glu and Phe standards (four different natural‐abundance levels) generated by this method and their accepted values have a linear regression slope of 1 and small intercepts demonstrating high accuracy. The precisions were 0.36‰–0.67‰ for Phe standards and 0.27‰–0.35‰ for Glu standards. Our method and the GC/C/IRMS approach produced equivalent δ15N values for two lab standards (McCarthy Lab AA mixture and cyanobacteria) within error. We further tested our method on a wide range of natural sample matrices and obtained reasonable results.
Our method provides a reliable alternative to the current methods for δ15N‐AA measurement as IC or HPLC‐based techniques that can collect underivatized AAs are widely available. Our chemical approach that converts AA to N2O can be easily implemented in laboratories currently analyzing δ15N of N2O using PT/CF/IRMS. This method will help promote the use of δ15N‐AA in important studies of N cycling and trophic ecology in a wide range of research areas.
It is imperative to understand how chemical preservation alters tissue isotopic compositions before using historical samples in ecological studies. Specifically, although compound‐specific isotope analysis of amino acids (CSIA‐AA) is becoming a widely used tool, there is little information on how preservation techniques affect amino acid
δ15N values. Methods
We evaluated the effects of chemical preservatives on bulk tissue
δ13C and δ15N and amino acid δ15N values, measured by gas chromatography/isotope ratio mass spectrometry (GC/IRMS), of (a) tuna ( ) and squid ( Thunnus albacares ) muscle tissues that were fixed in formaldehyde and stored in ethanol for 2 years and (b) two copepod species, Dosidicus gigas and Calanus pacificus , which were preserved in formaldehyde for 24–25 years. Eucalanus californicus Results
Tissues in formaldehyde‐ethanol had higher bulk
δ15N values (+1.4, ; +1.6‰, D. gigas ), higher T. albacares δ13C values for (+0.5‰), and lower D. gigas δ13C values for (−0.8‰) than frozen samples. The bulk T. albacares δ15N values from copepods were not different those from frozen samples, although the δ13C values from both species were lower (−1.0‰ for and −2.2‰ for E. californicus ) than those from frozen samples. The mean amino acid C. pacificus δ15N values from chemically preserved tissues were largely within 1‰ of those of frozen tissues, but the phenylalanine δ15N values were altered to a larger extent (range: 0.5–4.5‰). Conclusions
The effects of preservation on bulk
δ13C values were variable, where the direction and magnitude of change varied among taxa. The changes in bulk δ15N values associated with chemical preservation were mostly minimal, suggesting that storage in formaldehyde or ethanol will not affect the interpretation of δ15N values used in ecological studies. The preservation effects on amino acid δ15N values were also mostly minimal, mirroring bulk δ15N trends, which is promising for future CSIA‐AA studies of archived specimens. However, there were substantial differences in phenylalanine and valine δ15N values, which we speculate resulted from interference in the chromatographic resolution of unknown compounds rather than alteration of tissue isotopic composition due to chemical preservation.
Animals often consume resources from multiple energy channels, thereby connecting food webs and driving trophic structure. Such ‘multichannel feeding’ can dictate ecosystem function and stability, but tools to quantify this process are lacking. Stable isotope ‘fingerprints’ are unique patterns in essential amino acid (EAA) δ13C values that vary consistently between energy channels like primary production and detritus, and they have emerged as a tool to trace energy flow in wild systems. Because animals cannot synthesize EAAs de novo and must acquire them from dietary proteins, ecologists often assume δ13C fingerprints travel through food webs unaltered. Numerous studies have used this approach to quantify energy flow and multichannel feeding in animals, but δ13C fingerprinting has never been experimentally tested in a vertebrate consumer.
We tested the efficacy of δ13C fingerprinting using captive deer mice
Peromyscus maniculatusraised on diets containing bacterial, fungal and plant protein, as well as a combination of all three sources. We measured the transfer of δ13C fingerprints from diet to consumer liver, muscle and bone collagen, and we used linear discriminant analysis (LDA) and isotopic mixing models to estimate dietary proportions compared to known contributions. Lastly, we tested the use of published δ13C source fingerprints previously used to estimate energy flow and multichannel feeding by consumers.
We found that EAA δ13C values exhibit significant isotopic (i.e. trophic) fractionation between consumer tissues and diets. Nevertheless, LDA revealed that δ13C fingerprints are consistently routed and assimilated into consumer tissues, regardless of isotopic incorporation rate. Isotopic mixing models accurately estimated the proportional diets of consumers, but all models overestimated plant‐based protein contributions, likely due to the digestive efficiencies of protein sources. Lastly, we found that δ13C source fingerprints from published literature can lead to erroneous diet reconstruction.
We show that δ13C fingerprints accurately measure energy flow to vertebrate consumers across tissues with different isotopic incorporation rates, thereby enabling the estimation of multichannel feeding at various temporal scales. Our findings illustrate the power of δ13C fingerprinting for quantifying food web dynamics, but also reveal that careful selection of dietary source data is critical to the accuracy of this emerging technique.