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 acid δ 13 C (δ 13 C AA ) 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 acid δ 13 C (δ 13 C EAA ) fingerprints among four microalgal groups (mean posterior probability reclassification of 99.2 AE 2.9%). We also quantified temperature effects, a primary driver of microalgal bulk carbon isotope variability, on the fidelity of δ 13 C AA fingerprints. A 10 C range in temperature conditions did not have significant impacts on variance in δ 13 C AA values or the diagnostic microalgal δ 13 C EAA fingerprints. These δ 13 C EAA fingerprints 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.

The power and pitfalls of amino acid carbon stable isotopes for tracing the use and fate of basal resources in food webs

“…1). While environmental gradients may also specifically modify the fractionations associated with each AA synthesis pathway, these relative differences will likely be very small compared to the overall average effect of the environment on physiology, and therefore carbon acquisition (Stahl et al 2023, Larsen et al 2015, Fig. 2), represented here as Env.…”

“…In some studies, even finer distinctions in δ 13 C-EAA pattern distinctions have been observed between organs of individual plants, i.e. roots, seeds, and leaves (Lynch et al 2011 as well as among marine phytoplankton clades (Larsen et al 2020, Stahl et al 2023. Based on the available evidence, separation in δ 13 C-EAA patterns can be attributed to both phylogeny and phenotypic factors (section 2.2).…”

“…Recent work indicates that the δ 13 C-EAA patterns of Symbiodiniaceae may be distinctive from other free living dinoflagellates (Stahl et al 2023). Baseline δ 13 C-EAA values often vary between the symbionts and coral tissue but whether this translates to differing δ 13 C-EAA patterns is unclear (Martinez et al 2020, 2021.…”

“…Broad taxonomic groups such as algae, bacteria, fungi, and vascular plants each have characteristic δ 13 C-EAA patterns, i.e. the relative differences in δ 13 C values among a set of EAAs, that remain largely consistent across variable physical conditions, chemical conditions, and across time , 2015, Lynch et al 2016, Stahl et al 2023. Distinct δ 13 C-EAA patterns among basal organisms have been typically referred to as δ 13 C-EAA fingerprints .…”

Holocene climate change shifted Southern Ocean biogeochemical cycling and predator trophic dynamics

“…These regional changes in phytoplankton dynamics coincide with observed changes in krill and penguin populations (Montes-Hugo et al 2009). Future work using δ 13 C CSIA-AA fingerprinting (Stahl et al 2023) could help reconstructing changes in plankton community composition to explore this plankton community-driven food chain length hypothesis.…”