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Measuring repeatability of compositional diet estimates: An example using quantitative fatty acid signature analysis.
Connie Stewart1, Shelley L C Lang2,3, Sara Iverson2
1Department of Mathematics and Statistics University of New Brunswick Saint John Saint John New Brunswick Canada.
Ecology and Evolution
|October 31, 2022
Summary
Researchers developed a new method to measure predator diet consistency over time, crucial for understanding individual specialization and its ecological impacts. This approach handles complex diet data, improving ecological and food web analyses.
Area of Science:
- Ecology
- Quantitative Biology
- Bioinformatics
Background:
- Understanding predator diet consistency is vital for assessing individual specialization.
- Existing methods struggle with complex, zero-inflated diet data from techniques like quantitative fatty acid signature analysis (QFASA).
Purpose of the Study:
- To develop a novel statistical approach for measuring repeatability in multivariate compositional diet data.
- To extend existing repeatability measures to accommodate the complexities of predator diet estimations.
Main Methods:
- Proposed a new method extending univariate repeatability measures to multivariate compositional data using nonparametric multivariate analysis of variance.
- Incorporated nonparametric bootstrapping to develop confidence intervals accounting for sampling and measurement error in QFASA diet estimates.
- Ensured compatibility with both balanced and unbalanced datasets.
Main Results:
- The novel method provides reliable confidence intervals for repeatability, even with small sample sizes relative to the number of prey species.
- Demonstrated the method's efficacy using quantitative fatty acid signature analysis (QFASA) diet data from Northwest Atlantic grey seals.
Conclusions:
- The developed statistical framework offers a robust solution for analyzing temporal diet consistency in predators.
- This method has broad applicability to various compositional diet estimation techniques, enhancing ecological and food web research.
