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Stable isotope comparison between mantle and foot tissues of two freshwater unionids: Implications for food web studies

September 1, 2018

Unionid mussels are a key taxon for stable isotope studies of aquatic food webs, often serving as the primary integrator of the pelagic baseline. Past isotope studies with mussels have commonly used either foot tissue or mantle tissue, but no study has yet to quantify the relation of both carbon and nitrogen isotopes between these two tissue sources. This makes it difficult to justify cross-study comparisons when different tissue compartments and different species were used as the basis of food web models. Therefore, we collected foot and mantle tissues from two common mussel species, Amblema plicata and Fusconaia flava, from lotic and lentic sites in the Upper Mississippi and St. Croix rivers (Minnesota/Wisconsin). Paired tissue samples from each individual were analyzed for stable isotopes of nitrogen and carbon. There were strong relations between tissue types for both isotopes between species (r2 > 0.93). Paired t-tests indicated that there were statistically significant differences between the tissue sources in some instances, but the difference (0.04–0.21‰) was less than the analytical precision of the mass spectrometer (circa 0.2–0.3‰). We conclude that the isotopic values from these two tissue sources are biologically comparable and recommend that researchers use the tissue source and extraction technique that minimizes stress to the mussels. We also tested for significant differences between species within a site for either isotope or tissue type and found no statistically significant difference between species with the exception of carbon in foot tissue at two sites. The highly correlated isotopic response supports the interchangeable use of both tissue compartments and both species. These findings support comparisons between studies whether the results were based on either of these tissues or the two species studied. Comparability will also simplify sampling designs, save time, and save money for processing samples without diminishing the usefulness of the data.