Multi‐tissue stable‐isotope analyses can identify dietary specialization
Bibliographic record
Abstract
Summary Individual specialization along one or more niche axes is now recognized as an integral and ubiquitous aspect of populations. A major challenge, however, is quantifying the level of specialization using robust metrics that are applicable across species and ecosystems. Measuring stable‐isotope values in multiple tissues with different isotopic turnover rates could be one mechanism for quantifying specialization. We used simulation studies of stable‐isotope values to investigate how the recently proposed relative index of specialization varies in relation to variance in prey isotope values, diet–tissue discrimination factors, specialist group size and tissue half‐life, and applied specialization metrics to two systems – Australian freshwater fish and marine birds in the Canadian Arctic. In all simulations, populations comprised entirely of generalists were easily separated from those with even small amounts (5%) of individual specialization. In some cases, however, specialization measured using isotope values with bimodal distributions may appear similar to those with univariate distributions, but this can be detected by examining the original data. All fish and bird species examined showed varying degrees of individual specialization. Analysing stable isotopes in multiple tissues can provide a useful index of the degree of specialization within a population that can be compared to the same metric measured in other groups or species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".