How can stable isotope mixing models be used to quantify the proportional contributions of autochthonous and allochthonous carbon to freshwater food webs?

How can stable isotope mixing models be used to quantify the proportional contributions of autochthonous and allochthonous carbon to freshwater food webs?

• A. They rely on measuring water chemistry to estimate sources.
• B. Isotope signatures (δ13C, δ15N) from potential carbon sources are used in a mixing model to estimate the relative contributions to consumer tissues, accounting for trophic discrimination factors to partition the carbon budget.
• C. They require only isotopes from consumers and ignore source signatures.
• D. They are not applicable to carbon source estimation.

Answer: (B)

The key idea is using stable isotope mixing models to partition the carbon in consumers into autochthonous and allochthonous sources based on distinct isotopic fingerprints. By measuring the isotope signatures of potential carbon sources—typically δ13C to distinguish in-stream (autochthonous) from terrestrial (allochthonous) inputs, and δ15N to help place consumers on the trophic ladder—you can compare those source signatures to the isotopic composition of consumer tissues. The mixing model then estimates how much each source contributes to the consumer’s carbon budget, explicitly accounting for trophic discrimination factors, which describe how isotopes shift from diet to consumer tissue with each trophic step. This combination lets you quantify the relative influence of each carbon source on the food web. In practice, distinct source signatures are necessary; the model uses those signatures and the measured consumer values to solve for source proportions, while TEFs incorporate the predictable enrichment or depletion that occurs during assimilation. The result is a probabilistic estimate (when using Bayesian approaches) of each source’s contribution, including uncertainty. This approach is powerful because it integrates time-averaged dietary signals and provides a direct way to compare inputs from in-stream production versus terrestrial inputs, rather than relying on water chemistry alone or on consumer data without reference to source isotopes.

The key idea is using stable isotope mixing models to partition the carbon in consumers into autochthonous and allochthonous sources based on distinct isotopic fingerprints. By measuring the isotope signatures of potential carbon sources—typically δ13C to distinguish in-stream (autochthonous) from terrestrial (allochthonous) inputs, and δ15N to help place consumers on the trophic ladder—you can compare those source signatures to the isotopic composition of consumer tissues. The mixing model then estimates how much each source contributes to the consumer’s carbon budget, explicitly accounting for trophic discrimination factors, which describe how isotopes shift from diet to consumer tissue with each trophic step. This combination lets you quantify the relative influence of each carbon source on the food web.

In practice, distinct source signatures are necessary; the model uses those signatures and the measured consumer values to solve for source proportions, while TEFs incorporate the predictable enrichment or depletion that occurs during assimilation. The result is a probabilistic estimate (when using Bayesian approaches) of each source’s contribution, including uncertainty. This approach is powerful because it integrates time-averaged dietary signals and provides a direct way to compare inputs from in-stream production versus terrestrial inputs, rather than relying on water chemistry alone or on consumer data without reference to source isotopes.