Integrating DNA-Based Prey Occurrence Probability into Stable Isotope Mixing Models.

Area of Science:

• Ecology
• Molecular Biology
• Bioinformatics

Background:

• Laboratory methods like DNA barcoding and stable isotope analysis offer precise animal dietary insights.
• DNA barcoding identifies prey taxa, while stable isotopes quantify dietary contributions.
• Current limitations include DNA barcoding's inability to quantify prey and stable isotopes' reliance on a priori prey identification.

Purpose of the Study:

• To develop a novel method for integrating DNA-based dietary data into stable isotope mixing models.
• To address the limitations of current approaches that inaccurately quantify prey from DNA occurrence data.
• To accurately quantify animal diets by preserving the binomial nature of DNA data within Bayesian models.

Main Methods:

• Developed a new approach to incorporate DNA-based frequency of occurrence into Bayesian stable isotope mixing models.
• Utilized community-wide frequency of occurrence or logistic regression to inform prey inclusion probability in model iterations.
• Applied the method to quantify the diet of nestling Louisiana waterthrush (Parkesia motacilla).

Main Results:

• The novel method successfully integrates DNA-based dietary information into stable isotope mixing models.
• The approach preserves the binomial nature of DNA data, avoiding problematic normalization of occurrence frequencies.
• Demonstrated utility in quantifying the prey composition of a specific avian species.

Conclusions:

• The new method offers a more accurate way to quantify animal diets by combining DNA and stable isotope analyses.
• This approach overcomes previous limitations in integrating molecular and isotopic data for dietary studies.
• Accurate dietary quantification is crucial for understanding ecological interactions and conservation efforts.