The article discusses the effective application of stable isotope mixing models in food-web studies, highlighting the challenges and best practices for their use. The authors emphasize that the quality of mixing models is limited by the study design and data quality. Key recommendations include: 1. **Clarify Research Questions**: Studies should have clear objectives, be informed by system knowledge, and have robust sampling designs to characterize isotope variability. 2. **Choose Appropriate Models**: Researchers should select models that align with their research questions and be aware of their assumptions and limitations. 3. **Interpret Model Outputs Carefully**: Mixing models estimate proportions with uncertainty, so interpretations should be cautious. 4. **Use Prior Knowledge**: Understanding the diet and system is crucial for effective model application. 5. **Sample Collection**: Well-planned sampling is essential, considering the time period and tissue type that reflects the diet. 6. **Use Appropriate Diet-Tissue Discrimination Factors (DTDFs)**: DTDFs correct for systematic differences between tissues and diets, and their values can vary widely. 7. **Plot Data**: Graphical analysis helps ensure data compatibility and identifies potential issues. 8. **Include All Sources Informedly**: Excluding sources can bias estimates, and grouping sources may simplify results. 9. **Consider Concentration Dependence and Isotopic Routing**: Models can account for differences in elemental concentrations and dietary effects on fractionation. 10. **Incorporate Uncertainties**: Bayesian models can handle variability and measurement errors, providing more realistic estimates. 11. **Report Distributions**: Results should be reported as distributions rather than point estimates to reflect the uncertainty in the data. These guidelines aim to maximize the utility of stable isotope mixing models while acknowledging their limitations.The article discusses the effective application of stable isotope mixing models in food-web studies, highlighting the challenges and best practices for their use. The authors emphasize that the quality of mixing models is limited by the study design and data quality. Key recommendations include: 1. **Clarify Research Questions**: Studies should have clear objectives, be informed by system knowledge, and have robust sampling designs to characterize isotope variability. 2. **Choose Appropriate Models**: Researchers should select models that align with their research questions and be aware of their assumptions and limitations. 3. **Interpret Model Outputs Carefully**: Mixing models estimate proportions with uncertainty, so interpretations should be cautious. 4. **Use Prior Knowledge**: Understanding the diet and system is crucial for effective model application. 5. **Sample Collection**: Well-planned sampling is essential, considering the time period and tissue type that reflects the diet. 6. **Use Appropriate Diet-Tissue Discrimination Factors (DTDFs)**: DTDFs correct for systematic differences between tissues and diets, and their values can vary widely. 7. **Plot Data**: Graphical analysis helps ensure data compatibility and identifies potential issues. 8. **Include All Sources Informedly**: Excluding sources can bias estimates, and grouping sources may simplify results. 9. **Consider Concentration Dependence and Isotopic Routing**: Models can account for differences in elemental concentrations and dietary effects on fractionation. 10. **Incorporate Uncertainties**: Bayesian models can handle variability and measurement errors, providing more realistic estimates. 11. **Report Distributions**: Results should be reported as distributions rather than point estimates to reflect the uncertainty in the data. These guidelines aim to maximize the utility of stable isotope mixing models while acknowledging their limitations.