Trace metal accumulation as complementary dietary information for the isotopic analysis of complex food webs
Bibliographic record
Abstract
Summary Food web structure is a fundamental feature of ecosystems. Stable isotopes ( δ 15 N and δ 13 C) are used to estimate the relative contribution of food sources to consumer's diets (e.g. mixing models). In complex food webs, the use of δ 15 N and δ 13 C measurements cannot always solve trophic interactions and distinguish among aquatic organisms with different feeding habits if little intra‐ and interspecific isotopic differentiation occurs. We have developed a method to characterize trophic relationships using Bayesian stable isotope mixing models in combination with trace metal data as prior information. Trace metal information is useful because of the high correspondence between trace metal profiles in consumers and their food sources, as we show here in an example of concentrations of fish and their expected dietary items. Trace metal concentration allows a more accurate estimation of relative contributions of food sources to consumer species compared to estimates based only on stable isotope values. We show the improvement of the procedure using four freshwater fish species with well‐known feeding habits. The method provides a better estimation of the inter‐ and intraspecific dietary variability and correspondence with the feeding habits of these species. The approach described shows a considerable potential as a tool to assess trophic links in situations in which stable isotope methods are not conclusive. The method can be applied using other compounds that bioaccumulate in consumers (e.g. persistent organic pollutants).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".