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Abstract 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 micePeromyscus 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.
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Amino acid carbon isotope fingerprints are unique among eukaryotic microalgal taxonomic groups
Abstract Eukaryotic microalgae play critical roles in the structure and function of marine food webs. The contribution of microalgae to food webs can be tracked using compound‐specific isotope analysis of amino acids (CSIA‐AA). Previous CSIA‐AA studies have defined eukaryotic microalgae as a single functional group in food web mixing models, despite their vast taxonomic and ecological diversity. Using controlled cultures, this work characterizes the amino acidδ13C (δ13CAA) fingerprints—a multivariate metric of amino acid carbon isotope values—of four major groups of eukaryotic microalgae: diatoms, dinoflagellates, raphidophytes, and prasinophytes. We found excellent separation of essential amino acidδ13C (δ13CEAA) fingerprints among four microalgal groups (mean posterior probability reclassification of 99.2 ± 2.9%). We also quantified temperature effects, a primary driver of microalgal bulk carbon isotope variability, on the fidelity ofδ13CAAfingerprints. A 10°C range in temperature conditions did not have significant impacts on variance inδ13CAAvalues or the diagnostic microalgalδ13CEAAfingerprints. Theseδ13CEAAfingerprints were used to identify primary producers at the base of food webs supporting consumers in two contrasting systems: (1) penguins feeding in a diatom‐based food web and (2) mixotrophic corals receiving amino acids directly from autotrophic endosymbiotic dinoflagellates and indirectly from water column diatoms, prasinophytes, and cyanobacteria, likely via heterotrophic feeding on zooplankton. The increased taxonomic specificity of CSIA‐AA fingerprints developed here will greatly improve future efforts to reconstruct the contribution of diverse eukaryotic microalgae to the sources and cycling of organic matter in food web dynamics and biogeochemical cycling studies.
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- Award ID(s):
- 2049307
- PAR ID:
- 10419140
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Limnology and Oceanography
- Volume:
- 68
- Issue:
- 6
- ISSN:
- 0024-3590
- Page Range / eLocation ID:
- p. 1331-1345
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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