Using δ13C stable isotopes to quantify individual-level diet variation

Abstract

Individual-level diet variation can be easily quantified by gut-content analysis. However, because gut contents are a 'snapshot' of individuals' feeding habits, such cross-sectional data can be subject to sampling error and lead one to overestimate levels of diet variation. In contrast, stable isotopes reflect an individual's long-term diet, so isotope variation among individuals can be interpreted as diet variation. Nevertheless, population isotope variances alone cannot be directly compared among populations, because they depend on both the level of diet variation and the variance of prey isotope ratios. We developed a method to convert population isotope variances into a standardized index of individual specialization (WIC/TNW) that can be compared among populations, or to gut-content variation. We applied this method to diet and carbon isotope data of four species of frogs of the Brazilian savannah. Isotopes showed that gut contents provided a reliable measure of diet variation in three populations, but greatly overestimated diet variation in another population. Our method is sensitive to incomplete sampling of the prey and to among-individual variance in fractionation. Therefore, thorough sampling of prey and estimates of fractionation variance are desirable. Otherwise, the method is straightforward and provides a new tool for quantifying individual-level diet variation in natural populations that combines both gut-content and isotope data.

Stable isotope series from elephant ivory reveal lifetime histories of a true dietary generalist

“…At the individual level, morphological comparisons are only indirect niche proxies, whereas short-term studies based on observational data or analysis of gut contents give false impressions of specialization if niche differences are stochastic and non-persistent [4]. Long-term information is necessary to partition true levels of within-individual from between-individual dietary variation [5][6][7]. Longitudinal dietary data are obtainable through stable isotope analysis of fast-versus slow-growing tissues [6,23] and from isotopic profiles along inert tissues, which archive dietary chronologies in successive growth layers [8,9,22,24].…”

“…Long-term information is necessary to partition true levels of within-individual from between-individual dietary variation [5][6][7]. Longitudinal dietary data are obtainable through stable isotope analysis of fast-versus slow-growing tissues [6,23] and from isotopic profiles along inert tissues, which archive dietary chronologies in successive growth layers [8,9,22,24]. Tusks of elephants and other proboscideans are an ideal material for long-term investigations because they grow continuously throughout the animal's life, which may exceed 60 years.…”

Seasonal and ontogenetic shifts in the diet of Arctic charr Salvelinus alpinus in a subarctic lake

“…Hence, isotopic studies of fish trophic ecology are not subject to the same potential sampling errors as SCA, such as temporal changes in availability, patchy distribution and different digestion times of prey items Boecklen et al, 2011). Moreover, the range in d 13 C and d 15 N values, which is commonly referred to as the isotopic niche (see and references therein), can be used as a convenient measure of the trophic niche width of a population and the individual specialisation within Araú jo et al, 2007;. The long-term reliance of fish populations and individuals on littoral versus pelagic carbon can be studied using two-source isotopic mixing models (Layman et al, 2012 and references therein).…”

Factors Influencing Movements and Foraging Ecology of American Alligators (Alligator mississippiensis) in a Dynamic Subtropical Coastal Ecosystem

“…If BIC is larger than WIC for a given population, than the diet of the population is more different between individuals than they are within individuals. I divided BIC by TNW to generate (Bolnick et al 2002;Araujo et al 2007). The BIC/TNW calculations are biased by the inclusion of individuals that only contain one prey item in their stomach (Bolnick et al 2002), so I applied the following rules to each dataset to limit any bias in my INS results and produce conservative BIC/TNW values: 1) I removed all individuals with empty stomachs, 2) I removed all individuals with only one prey item in their stomach, and 3) I removed all individuals with only two prey items in their stomach, but only if both items were in the same prey category.…”

“…I accomplished this by testing each stomach contents dataset to see if they varied as a function of the independent variables capture season, capture year, capture location, size, and sex (Table 2; Araujo et al 2007). First, I applied all the sorting rules to each dataset and then I did a principal component analysis (PCA) on the proportions of prey use by individuals that were transformed using an arcsine-square root function (Araujo et al 2007).…”