Diet tracing in ecology: Method comparison and selection
Bibliographic record
Abstract
Abstract Determining diet is a key prerequisite for understanding species interactions, food web structure and ecological dynamics. In recent years, there has been considerable development in both the methodology and application of novel and more traditional dietary tracing methods, yet there is no comprehensive synthesis that systematically and quantitatively compares the different approaches. Here we conceptualise diet tracing in ecology, provide recommendations for method selection, and illustrate the advantages of method integration. We summarise empirical evidence on how different methods quantify diet mixtures, by contrasting estimates of dietary proportions from multiple methods applied to the same consumer‐resource datasets, or from experimental studies with known diet compositions. Our data synthesis revealed an urgent need for more experiential comparisons among the dietary methods. The comparison of diet quantifications from field observations showed that different techniques aligned well in cases with less than six diet items, but diverged considerably when applied to more complex diet mixtures. Efforts are ongoing to further advance dietary estimation, including how reliably compound specific stable isotope analyses and fatty acid profiles can quantify more prey items than bulk stable isotope analyses. Similarly, DNA analyses, which can depict trophic interactions at a higher resolution than any other method, are generating new ways to better quantify diets and differentiate among life‐stages of prey. Such efforts, combined with more empirical testing of each dietary method and establishment of open data repositories for dietary data, promise to greatly advance community and ecosystem ecology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.519 | 0.635 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".