The impact of protein quality on stable nitrogen isotope ratio discrimination and assimilated diet estimation
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- Robbins, C.T., Felicetti, L.A. & Florin, S.T. Oecologia (2010) 162: 571. doi:10.1007/s00442-009-1485-8
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Accurately predicting isotopic discrimination is central to estimating assimilated diets of wild animals when using stable isotopes. Current mixing models assume that the stable N isotope ratio (δ15N) discrimination (∆15N) for each food in a mixed diet is constant and independent of other foods being consumed. Thus, the discrimination value for the mixed diet is the combined, weighted average for each food when consumed as the sole diet. However, if protein quality is a major determinant of ∆15N, discrimination values for mixed diets may be higher or lower than the weighted average and will reflect the protein quality of the entire diet and not that of the individual foods. This potential difference occurs because the protein quality of a mixed diet depends on whether, and to what extent, the profiles and amounts of essential amino acids in the individual foods are complementary or non-complementary to each other in meeting the animal’s requirement. We tested these ideas by determining the ∆15N of several common foods (corn, wheat, alfalfa, soybean, and fish meal) with known amino acid profiles when fed singly and in combination to laboratory rats. Discrimination values for the mixed diets often differed from the weighted averages for the individual foods and depended on the degree of complementation. ∆15N for mixed diets ranged from 1.1‰ lower than the weighted average for foods with complementary amino acid profiles to 0.4‰ higher for foods with non-complementary amino acid profiles. These differences led to underestimates as high as 44% and overestimates as high as 36% of the relative proportions of fish meal and soybean meal N, respectively, in the assimilated mixed diets. We conclude that using isotopes to estimate assimilated diets is more complex than often appreciated and will require developing more biologically based, time-sensitive models.