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An unbiased method to estimate individual specialisation from multi‐tissue isotopic data
Author(s) -
Naya Daniel E.,
FrancoTrecu Valentina
Publication year - 2019
Publication title -
freshwater biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.297
H-Index - 156
eISSN - 1365-2427
pISSN - 0046-5070
DOI - 10.1111/fwb.13316
Subject(s) - resampling , statistics , context (archaeology) , variance (accounting) , trophic level , fish <actinopterygii> , population , biology , mathematics , ecology , paleontology , demography , accounting , fishery , sociology , business
Individual specialisation could affect several ecological and evolutionary processes. Assessing isotopic data from different tissues of a single individual (multi‐tissue approach) represents a common method to estimate individual trophic specialisation ( ITS ). However, a neglected problem with this approach is that isotopic values of two tissues from a single individual are not statistically independent, and hence, an underestimation of the within‐individual component of variance should be theoretically expected. In this study, we evaluate this potential problem by comparing ITS estimations as currently calculated (uncorrected ITS ) against ITS estimations based on a new method that considers the non‐independence problem (corrected ITS ). We used unpublished δ 15 N and δ 13 C data for nine fish species, together with previously published δ 15 N and δ 13 C data for eight other vertebrate species, to estimate (and compare) components of variance and ITS values, using uncorrected and corrected isotopic data. In addition, for each species, we used a Monte Carlo resampling routine to test the null hypothesis that all individuals sample equally from the population diet distribution. We found that the use of uncorrected δ 15 N values provided an average ITS estimation which is, depending on the overlap among tissues turnover rates, 14%–35% ( fish dataset) and 17%–40% ( all species dataset) lower than estimations based on corrected values. Similarly, the use of uncorrected δ 13 C values provided an average ITS estimation which is 12%–29% ( fish dataset) and 21%–45% ( all species dataset) lower than corrected estimations. The implications of these results in an ecological context are of great significance. For instance, the fish dataset showed that while uncorrected estimations indicate that three (δ 13 C) or four (δ 15 N) species are trophic specialists at the individual level, a moderate correction in isotopic values indicate that none (δ 13 C) or only one (δ 15 N) species is a trophic specialist at that level. Noticeably, this last result is much more congruent with dietary data obtained from stomach content analysis. Given the several pros of the multi‐tissue approach, such as its reduced operative costs, we suggest not to abandon this method, but to cope with the non‐independence problem by using the correction proposed here or, at least, by selecting body tissues with a minimal overlap in their turnover rates.