Combining stable isotopes, morphological, and molecular analyses to reconstruct the diet of free‐ranging consumers
Bibliographic record
Abstract
Accurate estimates of animal diet composition are essential to untangle complex interactions in food webs. Biomarkers and molecular tools are increasingly used to estimate diet, sometimes alongside traditional dietary tracing methods. Yet only a few empirical studies have compared the outcomes and potential gains of using a combination of these methods, especially using free-ranging animals with distinct foraging preferences.We used stable isotopes, morphological, and molecular analyses to investigate the diet of free-ranging consumers with two distinct diet types, that is, carnivore and omnivore. By combining the three analytical methods to assess the diet of consumers during the same period, we aimed to identify the limits of each method and to assess the potential benefits of their combined use to derive diet estimates.Our results showed that the different methods led to a consistent diet description for carnivores, which have a relatively simple diet mixture, but their outcomes somewhat differed for omnivore, which have a more complex diet. Still, the combined use of morphological and molecular analyses enhanced the diversity of food sources detected compared to the use of a single method independently of diet types. Precision of diet estimates derived from stable isotope analyses was improved by the addition of priors obtained from morphological and molecular diet analyses of the same population.Although we used free-ranging animals without a known diet, our empirical testing of three of the most widely used methods of diet determination highlights the limits of relying over a single approach, especially in systems with few or no a priori information about the foraging habits of consumers. The choice of an appropriate approach of diet description should be a key step when planning dietary studies of free-ranging populations. We recommend using more than one dietary determination methods especially for species with complex diet mixtures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".