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Diet‐tissue fractionation of δ 15 N by consumers from streams and rivers
Author(s) -
Bunn Stuart E.,
Leigh Catherine,
Jardine Timothy D.
Publication year - 2013
Publication title -
limnology and oceanography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 197
eISSN - 1939-5590
pISSN - 0024-3590
DOI - 10.4319/lo.2013.58.3.0765
Subject(s) - trophic level , fractionation , food web , streams , algae , stable isotope ratio , range (aeronautics) , environmental science , δ15n , isotopes of nitrogen , nitrogen , isotope , δ13c , invertebrate , isotope analysis , isotopes of carbon , environmental chemistry , ecology , biology , total organic carbon , chemistry , computer science , physics , computer network , organic chemistry , quantum mechanics , composite material , materials science
Variation in the diet‐tissue fractionation of stable nitrogen isotopes (Δ15N) is a major source of uncertainty in mixing model outputs and the calculation of trophic level in food web studies in aquatic systems. Using δ 15 N and δ 13 C of algae and consumers collected from a broad range of streams and rivers in Australia and New Guinea, we calculated Δ15N using a gradient approach, and compared these estimates with those from the literature. Riverine invertebrates and fishes from this region have δ 15 N diet‐tissue fractionation at the low end of the range of values reported for literature summaries, but different trophic groups had different Δ15N estimates. Source‐consumer regressions based on δ 13 C had lower slope estimates and lower R 2 values compared with those based on δ 15 N. This implies that although consumers on average obtain a portion of their organic carbon from higher plants, they derive most of their organic nitrogen from N‐rich algae. Use of appropriate Δ15N for riverine consumers will lead to more satisfactory results with isotope mixing models, and the uncertainty associated with Δ15N estimates can be incorporated in the latest iterations of these models that are probabilistic rather than deterministic.